bioframe

Bioframe is a library to enable flexible and scalable operations on genomic interval dataframes in python. Building bioframe directly on top of pandas enables immediate access to a rich set of dataframe operations. Working in python enables rapid visualization and iteration of genomic analyses.

Quickstart

Installation

$ pip install bioframe

To install the latest development version of bioframe from github, first make a local clone of the github repository:

$ git clone https://github.com/open2c/bioframe

Then, compile and install bioframe in development mode. This installs the package without moving it to a system folder, and thus allows for testing changes to the python code on the fly.

$ cd bioframe
$ pip install -e ./

Genomic interval operations

This guide provides an introdution into how to use bioframe to perform genomic interval operations. For the full list of genomic interval operations, see the API reference.

The following modules are used in this guide:

import itertools

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd

import bioframe as bf
import bioframe.vis

DataFrames & BedFrames

The core objects in bioframe are pandas DatFrames of genomic intervals, or BedFrames. These can either be defined directly with pandas.DataFrame:

df1 = pd.DataFrame([
    ['chr1', 1, 5],
    ['chr1', 3, 8],
    ['chr1', 8, 10],
    ['chr1', 12, 14]],
    columns=['chrom', 'start', 'end']
)

Or via functions in bioframe.core.construction, e.g.:

df2 = bioframe.from_any(
    [['chr1', 4, 8],
     ['chr1', 10, 11]], 
    name_col='chrom')

Or ingested from datasets and databases with functions in bioframe.io.fileops and bioframe.io.resources.

BedFrames satisfy the following properties:

• chrom, start, end columns

• columns have valid dtypes (object/string/categorical, int/pd.Int64Dtype(), int/pd.Int64Dtype())

• for each interval, if any of chrom, start, end are null, then all are null

• all starts <= ends.

Whether a dataframe satisfies these properties can be checked with bioframe.core.checks.is_bedframe():

bioframe.is_bedframe(df2)

True

See bioframe.core.checks for other functions that test properties of BedFrames and Technical Notes for detailed definitions. bioframe.core.construction.sanitize_bedframe() attempts to modfiy a DataFrame such that it satisfies bedFrame requirements.

bioframe.vis provides plotting utilities for intervals:

bf.vis.plot_intervals(df1, show_coords=True, xlim=(0,16))
plt.title('bedFrame1 intervals');

bf.vis.plot_intervals(df2, show_coords=True, xlim=(0,16), colors='lightpink')
plt.title('bedFrame2 intervals');

Overlap

Calculating the overlap between two sets of genomic intervals is a crucial genomic interval operation.

Using bioframe.overlap(), we can see the two dataframes defined above, df1 and df2, contain two pairs of overlapping intervals:

overlapping_intervals = bf.overlap(df1, df2, how='inner', suffixes=('_1','_2'))
display(overlapping_intervals)

	chrom_1	start_1	end_1	chrom_2	start_2	end_2
0	chr1	1	5	chr1	4	8
1	chr1	3	8	chr1	4	8

for i, reg_pair in overlapping_intervals.iterrows(): 
    bf.vis.plot_intervals_arr(
        starts = [reg_pair.start_1,reg_pair.start_2],
        ends = [reg_pair.end_1,reg_pair.end_2],
        colors = ['skyblue', 'lightpink'],
        levels = [2,1],
        xlim = (0,16),
        show_coords = True)
    plt.title(f'overlapping pair #{i}')

Note that we passed custom suffixes for the outputs (defaults are suffixes=("","_")), as well as a custom overlap mode (how='inner'). The default overlap mode, how='left' returns each interval in df1 whether or not it overlaps an interval in df2.

overlapping_intervals = bf.overlap(df1, df2)
display(overlapping_intervals)

	chrom	start	end	chrom_	start_	end_
0	chr1	1	5	chr1	4	8
1	chr1	3	8	chr1	4	8
2	chr1	8	10	None	<NA>	<NA>
3	chr1	12	14	None	<NA>	<NA>

Cluster

It is often useful to find overlapping intervals within a single set of genomic intervals. In bioframe, this is achieved with bioframe.cluster(). This function returns a DataFrame where subsets of overlapping intervals are assigned to the same group, reported in a new column.

To demonstrate the usage of bioframe.cluster(), we use the same df1 as above:

df1 = pd.DataFrame([
    ['chr1', 1, 5],
    ['chr1', 3, 8],
    ['chr1', 8, 10],
    ['chr1', 12, 14],
    ],
    columns=['chrom', 'start', 'end']
)

bf.vis.plot_intervals(df1, show_coords=True, xlim=(0,16))

Cluster returns a DataFrame where each interval is assigned to a group:

df_annotated = bf.cluster(df1, min_dist=0)
display(df_annotated)
bf.vis.plot_intervals(df_annotated, labels=df_annotated['cluster'], show_coords=True, xlim=(0,16))

	chrom	start	end	cluster	cluster_start	cluster_end
0	chr1	1	5	0	1	10
1	chr1	3	8	0	1	10
2	chr1	8	10	0	1	10
3	chr1	12	14	1	12	14

Note that using min_dist=0 and min_dist=None give different results, as the latter only clusters overlapping intervals and not adjacent intervals:

df_annotated = bf.cluster(df1, min_dist=None)
display(df_annotated)
bf.vis.plot_intervals(df_annotated, labels=df_annotated['cluster'], show_coords=True, xlim=(0,16))

	chrom	start	end	cluster	cluster_start	cluster_end
0	chr1	1	5	0	1	8
1	chr1	3	8	0	1	8
2	chr1	8	10	1	8	10
3	chr1	12	14	2	12	14

Extending the minimum distance to two (min_dist=2) makes all intervals part of the same cluster “0”:

df_annotated = bf.cluster(df1, min_dist=2)
display(df_annotated)
bf.vis.plot_intervals(df_annotated, labels=df_annotated['cluster'], show_coords=True, xlim=(0,16))

	chrom	start	end	cluster	cluster_start	cluster_end
0	chr1	1	5	0	1	14
1	chr1	3	8	0	1	14
2	chr1	8	10	0	1	14
3	chr1	12	14	0	1	14

Merge

Instead of returning cluster assignments, bioframe.merge() returns a new dataframe of merged genomic intervals. As with bioframe.cluster(), using min_dist=0 and min_dist=None gives different results.

If min_dist=0, this returns a dataframe of two intervals:

df_merged = bf.merge(df1, min_dist=0)

display(df_merged)
bf.vis.plot_intervals(df_merged, show_coords=True, xlim=(0,16))

	chrom	start	end	n_intervals
0	chr1	1	10	3
1	chr1	12	14	1

If min_dist=None, this returns a dataframe of three intervals:

df_merged = bf.merge(df1, min_dist=None)
display(df_merged)
bf.vis.plot_intervals(df_merged, show_coords=True, xlim=(0,16))

	chrom	start	end	n_intervals
0	chr1	1	8	2
1	chr1	8	10	1
2	chr1	12	14	1

Closest

In genomics, it is often useful not only to find features that overlap, but also features that are nearby along the genome. In bioframe, this is achieved using bioframe.closest().

closest_intervals = bf.closest(df1, df2, suffixes=('_1','_2'))
display(closest_intervals)

	chrom_1	start_1	end_1	chrom_2	start_2	end_2	distance
0	chr1	1	5	chr1	4	8	0
1	chr1	3	8	chr1	4	8	0
2	chr1	8	10	chr1	4	8	0
3	chr1	12	14	chr1	10	11	1

for i, reg_pair in closest_intervals.iterrows(): 
    bf.vis.plot_intervals_arr(
        starts = [reg_pair.start_1,reg_pair.start_2],
        ends = [reg_pair.end_1,reg_pair.end_2],
        colors = ['skyblue', 'lightpink'],
        levels = [2,1],
        xlim = (0,16),
        show_coords = True)
    plt.title(f'closest pair #{i}')

By default, bioframe.closest() reports overlapping intervals. This can be modified by passing ignore_overlap=True. Note the closest pair #2 and #3, which did not overlap, remain the same:

closest_intervals = bf.closest(df1, df2, ignore_overlaps=True, suffixes=('_1','_2'))
for i, reg_pair in closest_intervals.iterrows(): 
    bf.vis.plot_intervals_arr(
        starts = [reg_pair.start_1,reg_pair.start_2],
        ends = [reg_pair.end_1,reg_pair.end_2],
        colors = ['skyblue', 'lightpink'],
        levels = [2,1],
        xlim = (0,16),
        show_coords = True)
    plt.title(f'closest pair #{i}')

Closest intervals within a single DataFrame can be found simply by passing a single dataframe to bioframe.closest(). The number of closest intervals to report per query interval can be adjusted with k.

bf.closest(df1, k=2)

	chrom	start	end	chrom_	start_	end_	distance
0	chr1	1	5	chr1	3	8	0
1	chr1	1	5	chr1	8	10	3
2	chr1	3	8	chr1	1	5	0
3	chr1	3	8	chr1	8	10	0
4	chr1	8	10	chr1	3	8	0
5	chr1	8	10	chr1	12	14	2
6	chr1	12	14	chr1	8	10	2
7	chr1	12	14	chr1	3	8	4

Closest intervals upstream of the features in df1 can be found by ignoring downstream and overlaps. Upstream/downstream direction is defined by genomic coordinates by default (smaller coordinate is upstream).

bf.closest(df1, df2, 
    ignore_overlaps=True, 
    ignore_downstream=True)

	chrom	start	end	chrom_	start_	end_	distance
0	chr1	8	10	chr1	4	8	0
1	chr1	12	14	chr1	10	11	1
2	chr1	1	5	<NA>	<NA>	<NA>	<NA>
3	chr1	3	8	<NA>	<NA>	<NA>	<NA>

If the features in df1 have direction (e.g., genes have transcription direction), then the definition of upstream/downstream direction can be changed to the direction of the features by direction_col:

df1["strand"] = np.where(np.random.rand(len(df1)) > 0.5, "+", "-")
bf.closest(df1, df2, 
    ignore_overlaps=True, 
    ignore_downstream=True, 
    direction_col='strand')

	chrom	start	end	strand	chrom_	start_	end_	distance
0	chr1	8	10	-	chr1	10	11	0
1	chr1	1	5	+	<NA>	<NA>	<NA>	<NA>
2	chr1	3	8	+	<NA>	<NA>	<NA>	<NA>
3	chr1	12	14	-	<NA>	<NA>	<NA>	<NA>

Coverage & Count Overlaps

For two sets of genomic features, it is often useful to calculate the number of basepairs covered and the number of overlapping intervals. While these are fairly straightforward to compute from the output of bioframe.overlap() with pandas.groupby() and column renaming, since these are very frequently used, they are provided as core bioframe functions.

df1_coverage = bf.coverage(df1, df2)
display(df1_coverage)

	chrom	start	end	strand	coverage
0	chr1	1	5	+	1
1	chr1	3	8	+	4
2	chr1	8	10	-	0
3	chr1	12	14	-	0

df1_coverage_and_count = bf.count_overlaps(df1_coverage, df2)
display(df1_coverage_and_count)

	chrom	start	end	strand	coverage	count
0	chr1	1	5	+	1	1
1	chr1	3	8	+	4	1
2	chr1	8	10	-	0	0
3	chr1	12	14	-	0	0

This plot shows the coverage and number of overlaps for intervals in df1 by df2:

bf.vis.plot_intervals(
    df1_coverage_and_count, 
    show_coords=True, xlim=(0,16), 
    labels = [f'{cov} bp, {n} intervals' 
              for cov, n in zip(df1_coverage_and_count.coverage, df1_coverage_and_count['count'])])

bf.vis.plot_intervals(df2, show_coords=True, xlim=(0,16), colors='lightpink')

Subtract & Set Difference

Bioframe has two functions for computing differences between sets of intervals: at the level of basepairs and at the level of whole intervals.

Basepair-level subtraction is performed with bioframe.subtract():

subtracted_intervals = bf.subtract(df1, df2)
display(subtracted_intervals)

	chrom	start	end	strand
0	chr1	1	4	+
1	chr1	3	4	+
2	chr1	8	10	-
3	chr1	12	14	-

bf.vis.plot_intervals(subtracted_intervals, show_coords=True, xlim=(0,16))

Interval-level differences are calculated with bioframe.setdiff():

setdiff_intervals = bf.setdiff(df1, df2)
display(setdiff_intervals)

	chrom	start	end	strand
2	chr1	8	10	-
3	chr1	12	14	-

bf.vis.plot_intervals(setdiff_intervals, show_coords=True, xlim=(0,16))

Expand

bioframe.expand() enables quick resizing of intervals.

Expand supports additive resizing, with pad. Note that unless subsequently trimmed (with bioframe.trim()), expanded intervals can have negative values:

expanded_intervals = bf.expand(df1, pad=2)
display(expanded_intervals)

	chrom	start	end	strand
0	chr1	-1	7	+
1	chr1	1	10	+
2	chr1	6	12	-
3	chr1	10	16	-

bf.vis.plot_intervals(expanded_intervals, show_coords=True, xlim=(0,16))

Expand also supports multiplicative resizing, with scale. Note that scale=0 resizes all intervals to their midpoints:

expanded_intervals = bf.expand(df1, scale=0)
display(expanded_intervals)

	chrom	start	end	strand
0	chr1	3	3	+
1	chr1	6	6	+
2	chr1	9	9	-
3	chr1	13	13	-

bf.vis.plot_intervals(expanded_intervals, show_coords=True, xlim=(0,16))

Genomic Views

Certain interval operations are often used relative to a set of reference intervals, whether those are chromosomes, scaffolds, or sub-intervals of either. Bioframe formalizes this with the concept of a genomic view, implemented as pandas dataframes, termed viewFrames, that satisfy the following:

• all requirements for bedFrames, including columns for (‘chrom’, ‘start’, ‘end’)

• it has an additional column, view_name_col, with default ‘name’

• entries in the view_name_col are unique

• intervals are non-overlapping

• it does not contain null values

Importanly a genomic view specifies a global coordinate axis, i.e. a conversion from a genomic coordinate system to a single axis. See Technical Notes for more details.

Bioframe provides a function to assign intervals to corresponding intervals in a view, bioframe.assign_view(), a function to construct views from various input formats, bioframe.core.construction.make_viewframe(), and a function check that viewframe requirements are met, bioframe.core.checks.is_viewframe().

The following genomic interval operations make use of views, though also have useful default behavior if no view is provided: bioframe.complement(), bioframe.trim(), bioframe.sort_bedframe().

Complement

Equally important to finding which genomic features overlap is finding those that do not. bioframe.complement() returns a BedFrame of intervals not covered by any intervals in an input BedFrame.

Complments are defined relative to a genomic view. Here this is provided as a dictionary with a single chromosome of length 15:

df_complemented = bf.complement(df1, view_df={'chr1':15})
display(df_complemented)

	chrom	start	end	view_region
0	chr1	0	1	chr1
1	chr1	10	12	chr1
2	chr1	14	15	chr1

bf.vis.plot_intervals(df_complemented, show_coords=True, xlim=(0,16), colors='lightpink')

If no view is provided, complement is calculated per unique chromosome in the input with right limits of np.iinfo(np.int64).max.

df_complemented = bf.complement(df1)
display(df_complemented)

	chrom	start	end	view_region
0	chr1	0	1	chr1
1	chr1	10	12	chr1
2	chr1	14	9223372036854775807	chr1

Trim

Certain regions are often best avoided for genomic analyses. bioframe.trim() trims intervals to a specified view. Intervals falling outside of view regions have their filled with null values.

view_df = pd.DataFrame(
    [
        ["chr1", 0, 4, "chr1p"],
        ["chr1", 5, 9, "chr1q"],
        ["chrX", 0, 50, "chrX"],
        ["chrM", 0, 10, "chrM"]],
    columns=["chrom", "start", "end", "name"],
)

trimmed_intervals = bf.trim(df1, view_df)
display(trimmed_intervals)

	chrom	start	end	strand
0	chr1	1.0	4.0	+
1	chr1	5.0	8.0	+
2	chr1	8.0	9.0	-
3	<NA>	NaN	NaN	-

Note that the last interval of df1 fell beyond ‘chr1q’ and is now null, and the last interval now ends at 9 instead of 10.

bf.vis.plot_intervals(trimmed_intervals, show_coords=True, xlim=(0,16))

If no view is provided, this function trims intervals at zero to avoid negative values.

Sorting

If no view is provided, bioframe.sort_bedframe() sorts by (“chrom”, “start”, “end”) columns:

df_unsorted = pd.DataFrame([
    ['chrM', 3, 8],
    ['chrM', 1, 5],
    ['chrX', 12, 14],
    ['chrX', 8, 10]],
    columns=['chrom', 'start', 'end']
)

display( bf.sort_bedframe(df_unsorted) )

	chrom	start	end
0	chrM	1	5
1	chrM	3	8
2	chrX	8	10
3	chrX	12	14

Views enable a specifying a sort order on a set of intervals. This flexibility is useful when the desired sorting order is non-lexicographical, e.g. with chrM after autosomes and chrX:

display( bf.sort_bedframe(df_unsorted, view_df) )

	chrom	start	end
0	chrX	8	10
1	chrX	12	14
2	chrM	1	5
3	chrM	3	8

Selecting & Slicing

Since bioFrame operates directly with pandas DataFrames, all typical selection and slicing operations are directly relevant.

Bioframe also provides a function bioframe.select() that enables selecting interval subsets using UCSC string format:

display( bioframe.select(df_unsorted,'chrX:8-14') )

	chrom	start	end
2	chrX	12	14
3	chrX	8	10

Flexible column naming

Genomic analyses often deal with dataframes with inhomogeneously named columns. Bioframe offers a way to set the default column names that are most convenient for your analyses.

Default bedframe column names are stored in bioframe.core.specs_rc.

bf.core.specs._rc

{'colnames': {'chrom': 'chrom', 'start': 'start', 'end': 'end'}}

If the dataframes we wish to work with have ['CHROMOSOME', 'LEFT', 'RIGHT'], we can either pass cols to operations in bioframe.ops:

df1_diff_colnames = pd.DataFrame([
    ['chr1', 1, 5],
    ['chr1', 3, 8]],
    columns=['CHROMOSOME', 'LEFT', 'RIGHT']
)

df2_diff_colnames = pd.DataFrame([
    ['chr1', 4, 8],
    ['chr1', 10, 11]],
    columns=['CHROMOSOME', 'LEFT', 'RIGHT']
)

bf.overlap(
    df1_diff_colnames, df2_diff_colnames,
    cols1=['CHROMOSOME', 'LEFT', 'RIGHT'],
    cols2=['CHROMOSOME', 'LEFT', 'RIGHT'],
)

	CHROMOSOME	LEFT	RIGHT	CHROMOSOME_	LEFT_	RIGHT_
0	chr1	1	5	chr1	4	8
1	chr1	3	8	chr1	4	8

Or, we can update the default column names:

with bf.core.specs.update_default_colnames(['CHROMOSOME', 'LEFT', 'RIGHT']):
    display(bf.overlap(
        df1_diff_colnames, df2_diff_colnames,
    ))

	CHROMOSOME	LEFT	RIGHT	CHROMOSOME_	LEFT_	RIGHT_
0	chr1	1	5	chr1	4	8
1	chr1	3	8	chr1	4	8

# setting colnames back to default.
bf.core.specs.update_default_colnames(['chrom', 'start', 'end'])
bf.core.specs._rc

{'colnames': {'chrom': 'chrom', 'start': 'start', 'end': 'end'}}

Reading genomic dataframes

import bioframe

Bioframe provides multiple methods to convert data stored in common genomic file formats to pandas dataFrames in bioframe.io.

Reading tabular text data

The most common need is to read tablular data, which can be accomplished with bioframe.read_table. This function wraps pandas pandas.read_csv/pandas.read_table (tab-delimited by default), but allows the user to easily pass a schema (i.e. list of pre-defined column names) for common genomic interval-based file formats.

For example,

df = bioframe.read_table(
    'https://www.encodeproject.org/files/ENCFF001XKR/@@download/ENCFF001XKR.bed.gz', 
    schema='bed9'
)
display(df[0:3])

	chrom	start	end	name	score	strand	thickStart	thickEnd	rgb
0	chr1	193500	194500	.	400	+	.	.	179,45,0
1	chr1	618500	619500	.	700	+	.	.	179,45,0
2	chr1	974500	975500	.	1000	+	.	.	179,45,0

df = bioframe.read_table(
    "https://www.encodeproject.org/files/ENCFF401MQL/@@download/ENCFF401MQL.bed.gz", 
     schema='narrowPeak')
display(df[0:3])

	chrom	start	end	name	score	strand	fc	-log10p	-log10q	relSummit
0	chr19	48309541	48309911	.	1000	.	5.04924	-1.0	0.00438	185
1	chr4	130563716	130564086	.	993	.	5.05052	-1.0	0.00432	185
2	chr1	200622507	200622877	.	591	.	5.05489	-1.0	0.00400	185

df = bioframe.read_table(
    'https://www.encodeproject.org/files/ENCFF001VRS/@@download/ENCFF001VRS.bed.gz', 
     schema='bed12'
)
display(df[0:3])

	chrom	start	end	name	score	strand	thickStart	thickEnd	rgb	blockCount	blockSizes	blockStarts
0	chr19	54331773	54620705	5C_304_ENm007_FOR_1.5C_304_ENm007_REV_40	1000	.	54331773	54620705	0	2	14528,19855,	0,269077,
1	chr19	54461360	54620705	5C_304_ENm007_FOR_26.5C_304_ENm007_REV_40	1000	.	54461360	54620705	0	2	800,19855,	0,139490,
2	chr5	131346229	132145236	5C_299_ENm002_FOR_241.5C_299_ENm002_REV_33	1000	.	131346229	132145236	0	2	2609,2105,	0,796902,

The schema argument looks up file type from a registry of schemas stored in the bioframe.SCHEMAS dictionary:

bioframe.SCHEMAS['bed6']

['chrom', 'start', 'end', 'name', 'score', 'strand']

UCSC Big Binary Indexed files (BigWig, BigBed)

Bioframe also has convenience functions for reading and writing bigWig and bigBed binary files to and from pandas DataFrames.

bw_url = 'http://genome.ucsc.edu/goldenPath/help/examples/bigWigExample.bw'
df = bioframe.read_bigwig(bw_url, "chr21", start=10_000_000, end=10_010_000)
df.head(5)

/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:387: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)
/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:387: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)
/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:387: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)
/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:387: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)

	chrom	start	end	value
0	chr21	10000000	10000005	40.0
1	chr21	10000005	10000010	40.0
2	chr21	10000010	10000015	60.0
3	chr21	10000015	10000020	80.0
4	chr21	10000020	10000025	40.0

df['value'] *= 100
df.head(5)

	chrom	start	end	value
0	chr21	10000000	10000005	4000.0
1	chr21	10000005	10000010	4000.0
2	chr21	10000010	10000015	6000.0
3	chr21	10000015	10000020	8000.0
4	chr21	10000020	10000025	4000.0

chromsizes = bioframe.fetch_chromsizes('hg19')
# bioframe.to_bigwig(df, chromsizes, 'times100.bw')  

# note: requires UCSC bedGraphToBigWig binary, which can be installed as
# !conda install -y -c bioconda ucsc-bedgraphtobigwig

bb_url = 'http://genome.ucsc.edu/goldenPath/help/examples/bigBedExample.bb'
bioframe.read_bigbed(bb_url, "chr21", start=48000000).head()

/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:444: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)
/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:444: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)
/home/docs/checkouts/readthedocs.org/user_builds/bioframe/envs/stable/lib/python3.10/site-packages/bioframe/io/fileops.py:444: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  df = f.fetch_intervals(chrom, start=start, end=end)

	chrom	start	end
0	chr21	48003453	48003785
1	chr21	48003545	48003672
2	chr21	48018114	48019432
3	chr21	48018244	48018550
4	chr21	48018843	48019099

Reading genome assembly information

The most fundamental information about a genome assembly is its set of chromosome sizes.

Bioframe provides functions to read chromosome sizes file as pandas.Series, with some useful filtering and sorting options:

bioframe.read_chromsizes(
    'https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes'
)

chr1     248956422
chr2     242193529
chr3     198295559
chr4     190214555
chr5     181538259
chr6     170805979
chr7     159345973
chr8     145138636
chr9     138394717
chr10    133797422
chr11    135086622
chr12    133275309
chr13    114364328
chr14    107043718
chr15    101991189
chr16     90338345
chr17     83257441
chr18     80373285
chr19     58617616
chr20     64444167
chr21     46709983
chr22     50818468
chrX     156040895
chrY      57227415
chrM         16569
Name: length, dtype: int64

bioframe.read_chromsizes('https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes',
                         filter_chroms=False)

chr1                248956422
chr2                242193529
chr3                198295559
chr4                190214555
chr5                181538259
                      ...    
chrUn_KI270539v1          993
chrUn_KI270385v1          990
chrUn_KI270423v1          981
chrUn_KI270392v1          971
chrUn_KI270394v1          970
Name: length, Length: 455, dtype: int64

dm6_url = 'https://hgdownload.soe.ucsc.edu/goldenPath/dm6/database/chromInfo.txt.gz'

bioframe.read_chromsizes(dm6_url,
                         filter_chroms=True, 
                         chrom_patterns=("^chr2L$", "^chr2R$", "^chr3L$", "^chr3R$", "^chr4$", "^chrX$")
)

chr2L    23513712
chr2R    25286936
chr3L    28110227
chr3R    32079331
chr4      1348131
chrX     23542271
Name: length, dtype: int64

bioframe.read_chromsizes(dm6_url, chrom_patterns=["^chr\d+L$", "^chr\d+R$", "^chr4$", "^chrX$", "^chrM$"])

chr2L    23513712
chr3L    28110227
chr2R    25286936
chr3R    32079331
chr4      1348131
chrX     23542271
chrM        19524
Name: length, dtype: int64

Bioframe provides a convenience function to fetch chromosome sizes from UCSC given an assembly name:

chromsizes = bioframe.fetch_chromsizes('hg38')
chromsizes[-5:]

name
chr21     46709983
chr22     50818468
chrX     156040895
chrY      57227415
chrM         16569
Name: length, dtype: int64

Bioframe can also generate a list of centromere positions using information from some UCSC assemblies:

display(
    bioframe.fetch_centromeres('hg38')[:3]
)

	chrom	start	end	mid
0	chr1	121700000	125100000	123400000
1	chr2	91800000	96000000	93900000
2	chr3	87800000	94000000	90900000

These functions are just wrappers for a UCSC client. Users can also use UCSCClient directly:

client = bioframe.UCSCClient('hg38')
client.fetch_cytoband()

	chrom	start	end	name	gieStain
0	chr1	0	2300000	p36.33	gneg
1	chr1	2300000	5300000	p36.32	gpos25
2	chr1	5300000	7100000	p36.31	gneg
3	chr1	7100000	9100000	p36.23	gpos25
4	chr1	9100000	12500000	p36.22	gneg
...	...	...	...	...	...
1544	chr19_MU273387v1_alt	0	89211	NaN	gneg
1545	chr16_MU273376v1_fix	0	87715	NaN	gneg
1546	chrX_MU273393v1_fix	0	68810	NaN	gneg
1547	chr8_MU273360v1_fix	0	39290	NaN	gneg
1548	chr5_MU273352v1_fix	0	34400	NaN	gneg

1549 rows × 5 columns

Curated genome assembly build information

New in v0.5.0

Bioframe also has locally stored information for common genome assembly builds.

For a given provider and assembly build, this API provides additional sequence metadata:

• A canonical name for every sequence, usually opting for UCSC-style naming.

• A canonical ordering of the sequences.

• Each sequence’s length.

• An alias dictionary mapping alternative names or aliases to the canonical sequence name.

• Each sequence is assigned to an assembly unit: e.g., primary, non-nuclear, decoy.

• Each sequence is assigned a role: e.g., assembled molecule, unlocalized, unplaced.

bioframe.assemblies_available()

	organism	provider	provider_build	release_year	seqinfo	cytobands	default_roles	default_units	url
0	homo sapiens	ncbi	GRCh37	2009	hg19.seqinfo.tsv	hg19.cytoband.tsv	[assembled]	[primary, non-nuclear-revised]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/0...
1	homo sapiens	ucsc	hg19	2009	hg19.seqinfo.tsv	hg19.cytoband.tsv	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/hg1...
2	homo sapiens	ncbi	GRCh38	2013	hg38.seqinfo.tsv	hg38.cytoband.tsv	[assembled]	[primary, non-nuclear]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/0...
3	homo sapiens	ucsc	hg38	2013	hg38.seqinfo.tsv	hg38.cytoband.tsv	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/hg3...
4	homo sapiens	ncbi	T2T-CHM13v2.0	2022	hs1.seqinfo.tsv	hs1.cytoband.tsv	[assembled]	[primary, non-nuclear]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/0...
5	homo sapiens	ucsc	hs1	2022	hs1.seqinfo.tsv	hs1.cytoband.tsv	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/hs1...
6	mus musculus	ncbi	MGSCv37	2010	mm9.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/0...
7	mus musculus	ucsc	mm9	2007	mm9.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/mm9...
8	mus musculus	ncbi	GRCm38	2011	mm10.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/0...
9	mus musculus	ucsc	mm10	2011	mm10.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/mm1...
10	mus musculus	ncbi	GRCm39	2020	mm39.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/0...
11	mus musculus	ucsc	mm39	2020	mm39.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/mm3...
12	drosophila melanogaster	ucsc	dm3	2006	dm3.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/dm3...
13	drosophila melanogaster	ucsc	dm6	2014	dm6.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/dm6...
14	caenorhabditis elegans	ucsc	ce10	2010	ce10.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/ce1...
15	caenorhabditis elegans	ucsc	ce11	2013	ce11.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/ce1...
16	danio rerio	ucsc	danRer10	2014	danRer10.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/dan...
17	danio rerio	ucsc	danRer11	2017	danRer10.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/dan...
18	saccharomyces cerevisiae	ucsc	sacCer3	2011	sacCer3.seqinfo.tsv	NaN	[assembled]	[primary, non-nuclear]	https://hgdownload.soe.ucsc.edu/goldenPath/sac...

hg38 = bioframe.assembly_info("hg38")
print(hg38.provider, hg38.provider_build)
hg38.seqinfo

ucsc hg38

	name	length	role	molecule	unit	aliases
0	chr1	248956422	assembled	chr1	primary	1,CM000663.2,NC_000001.11
1	chr2	242193529	assembled	chr2	primary	2,CM000664.2,NC_000002.12
2	chr3	198295559	assembled	chr3	primary	3,CM000665.2,NC_000003.12
3	chr4	190214555	assembled	chr4	primary	4,CM000666.2,NC_000004.12
4	chr5	181538259	assembled	chr5	primary	5,CM000667.2,NC_000005.10
5	chr6	170805979	assembled	chr6	primary	6,CM000668.2,NC_000006.12
6	chr7	159345973	assembled	chr7	primary	7,CM000669.2,NC_000007.14
7	chr8	145138636	assembled	chr8	primary	8,CM000670.2,NC_000008.11
8	chr9	138394717	assembled	chr9	primary	9,CM000671.2,NC_000009.12
9	chr10	133797422	assembled	chr10	primary	10,CM000672.2,NC_000010.11
10	chr11	135086622	assembled	chr11	primary	11,CM000673.2,NC_000011.10
11	chr12	133275309	assembled	chr12	primary	12,CM000674.2,NC_000012.12
12	chr13	114364328	assembled	chr13	primary	13,CM000675.2,NC_000013.11
13	chr14	107043718	assembled	chr14	primary	14,CM000676.2,NC_000014.9
14	chr15	101991189	assembled	chr15	primary	15,CM000677.2,NC_000015.10
15	chr16	90338345	assembled	chr16	primary	16,CM000678.2,NC_000016.10
16	chr17	83257441	assembled	chr17	primary	17,CM000679.2,NC_000017.11
17	chr18	80373285	assembled	chr18	primary	18,CM000680.2,NC_000018.10
18	chr19	58617616	assembled	chr19	primary	19,CM000681.2,NC_000019.10
19	chr20	64444167	assembled	chr20	primary	20,CM000682.2,NC_000020.11
20	chr21	46709983	assembled	chr21	primary	21,CM000683.2,NC_000021.9
21	chr22	50818468	assembled	chr22	primary	22,CM000684.2,NC_000022.11
22	chrX	156040895	assembled	chrX	primary	X,CM000685.2,NC_000023.11
23	chrY	57227415	assembled	chrY	primary	Y,CM000686.2,NC_000024.10
24	chrM	16569	assembled	chrM	non-nuclear	MT,J01415.2,NC_012920.1

hg38.chromsizes

name
chr1     248956422
chr2     242193529
chr3     198295559
chr4     190214555
chr5     181538259
chr6     170805979
chr7     159345973
chr8     145138636
chr9     138394717
chr10    133797422
chr11    135086622
chr12    133275309
chr13    114364328
chr14    107043718
chr15    101991189
chr16     90338345
chr17     83257441
chr18     80373285
chr19     58617616
chr20     64444167
chr21     46709983
chr22     50818468
chrX     156040895
chrY      57227415
chrM         16569
Name: length, dtype: int64

hg38.alias_dict["MT"]

'chrM'

bioframe.assembly_info("hg38", roles="all").seqinfo

	name	length	role	molecule	unit	aliases
0	chr1	248956422	assembled	chr1	primary	1,CM000663.2,NC_000001.11
1	chr2	242193529	assembled	chr2	primary	2,CM000664.2,NC_000002.12
2	chr3	198295559	assembled	chr3	primary	3,CM000665.2,NC_000003.12
3	chr4	190214555	assembled	chr4	primary	4,CM000666.2,NC_000004.12
4	chr5	181538259	assembled	chr5	primary	5,CM000667.2,NC_000005.10
...	...	...	...	...	...	...
189	chrUn_KI270753v1	62944	unplaced	NaN	primary	HSCHRUN_RANDOM_CTG30,KI270753.1,NT_187508.1
190	chrUn_KI270754v1	40191	unplaced	NaN	primary	HSCHRUN_RANDOM_CTG33,KI270754.1,NT_187509.1
191	chrUn_KI270755v1	36723	unplaced	NaN	primary	HSCHRUN_RANDOM_CTG34,KI270755.1,NT_187510.1
192	chrUn_KI270756v1	79590	unplaced	NaN	primary	HSCHRUN_RANDOM_CTG35,KI270756.1,NT_187511.1
193	chrUn_KI270757v1	71251	unplaced	NaN	primary	HSCHRUN_RANDOM_CTG36,KI270757.1,NT_187512.1

194 rows × 6 columns

Contributing metadata for a new assembly build

To contribute a new assembly build to bioframe’s internal metadata registry, make a pull request with the following items:

1. Add a record to the assembly manifest file located at bioframe/io/data/_assemblies.yml. Required fields are as shown in the example below.

2. Create a seqinfo.tsv file for the new assembly build and place it in bioframe/io/data. Reference the exact file name in the manifest record’s seqinfo field. The seqinfo is a tab-delimited file with a required header line as shown in the example below.

3. Optionally, a cytoband.tsv file adapted from a cytoBand.txt file from UCSC.

Note that we currently do not include sequences with alt or patch roles in seqinfo files.

Example

Metadata for the mouse mm9 assembly build as provided by UCSC.

_assemblies.yml

...
- organism: mus musculus
  provider: ucsc
  provider_build: mm9
  release_year: 2007
  seqinfo: mm9.seqinfo.tsv
  default_roles: [assembled]
  default_units: [primary, non-nuclear]
  url: https://hgdownload.soe.ucsc.edu/goldenPath/mm9/bigZips/
...

mm9.seqinfo.tsv

name	length	role	molecule	unit	aliases
chr1	197195432	assembled	chr1	primary	1,CM000994.1,NC_000067.5
chr2	181748087	assembled	chr2	primary	2,CM000995.1,NC_000068.6
chr3	159599783	assembled	chr3	primary	3,CM000996.1,NC_000069.5
chr4	155630120	assembled	chr4	primary	4,CM000997.1,NC_000070.5
chr5	152537259	assembled	chr5	primary	5,CM000998.1,NC_000071.5
chr6	149517037	assembled	chr6	primary	6,CM000999.1,NC_000072.5
chr7	152524553	assembled	chr7	primary	7,CM001000.1,NC_000073.5
chr8	131738871	assembled	chr8	primary	8,CM001001.1,NC_000074.5
chr9	124076172	assembled	chr9	primary	9,CM001002.1,NC_000075.5
chr10	129993255	assembled	chr10	primary	10,CM001003.1,NC_000076.5
chr11	121843856	assembled	chr11	primary	11,CM001004.1,NC_000077.5
chr12	121257530	assembled	chr12	primary	12,CM001005.1,NC_000078.5
chr13	120284312	assembled	chr13	primary	13,CM001006.1,NC_000079.5
chr14	125194864	assembled	chr14	primary	14,CM001007.1,NC_000080.5
chr15	103494974	assembled	chr15	primary	15,CM001008.1,NC_000081.5
chr16	98319150	assembled	chr16	primary	16,CM001009.1,NC_000082.5
chr17	95272651	assembled	chr17	primary	17,CM001010.1,NC_000083.5
chr18	90772031	assembled	chr18	primary	18,CM001011.1,NC_000084.5
chr19	61342430	assembled	chr19	primary	19,CM001012.1,NC_000085.5
chrX	166650296	assembled	chrX	primary	X,CM001013.1,NC_000086.6
chrY	15902555	assembled	chrY	primary	Y,CM001014.1,NC_000087.6
chrM	16299	assembled	chrM	non-nuclear	MT,AY172335.1,NC_005089.1
chr1_random	1231697	unlocalized	chr1	primary	
chr3_random	41899	unlocalized	chr3	primary	
chr4_random	160594	unlocalized	chr4	primary	
chr5_random	357350	unlocalized	chr5	primary	
chr7_random	362490	unlocalized	chr7	primary	
chr8_random	849593	unlocalized	chr8	primary	
chr9_random	449403	unlocalized	chr9	primary	
chr13_random	400311	unlocalized	chr13	primary	
chr16_random	3994	unlocalized	chr16	primary	
chr17_random	628739	unlocalized	chr17	primary	
chrX_random	1785075	unlocalized	chrX	primary	
chrY_random	58682461	unlocalized	chrY	primary	
chrUn_random	5900358	unplaced		primary	

Reading other genomic formats

See the docs for File I/O for other supported file formats.

Performance

This notebook illustrates performance of typical use cases for bioframe on sets of randomly generated intervals.

# ! pip install pyranges
## Optional: 
# ! conda install -c bioconda bedtools
# ! pip install pybedtools

import platform
import psutil

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.facecolor']='white'
plt.rcParams['font.size']=16

import bioframe
import pyranges

# Note that by default we switch off the demo of pybedtools.
# It runs for minutes for 10^5 intervals
include_pybedtools = False
if include_pybedtools: 
    import pybedtools
    pybedtools.helpers.set_bedtools_path(path='/usr/bin/') # Set the path to bedtools CLI

print(f"Bioframe v.{bioframe.__version__}")
print(f"PyRanges v.{pyranges.__version__}")
if include_pybedtools: 
    print(f"Pybedtools v.{pybedtools.__version__}")

print(f"System Platform: {platform.platform()}")
print(f"{psutil.cpu_count()} CPUs at {psutil.cpu_freq().current:.0f} GHz") 

Bioframe v.0.5.1
PyRanges v.0.0.129
System Platform: Linux-5.19.0-46-generic-x86_64-with-glibc2.35
24 CPUs at 3040 GHz

Below we define a function to generate random intervals with various properties, returning a dataframe of intervals.

def make_random_intervals(
        n=1e5, 
        n_chroms=1, 
        max_coord=None, 
        max_length=10, 
        sort=False,
        categorical_chroms=False,
        
    ):
    n = int(n)
    n_chroms = int(n_chroms)
    max_coord = (n // n_chroms) if max_coord is None else int(max_coord)
    max_length = int(max_length)
    
    chroms = np.array(['chr'+str(i+1) for i in range(n_chroms)])[
        np.random.randint(0, n_chroms, n)]
    starts = np.random.randint(0, max_coord, n)
    ends = starts + np.random.randint(1, max_length, n)

    df = pd.DataFrame({
        'chrom':chroms,
        'start':starts,
        'end':ends
    })
    
    if categorical_chroms:
        df['chrom'] = df['chrom'].astype('category')

    if sort:
        df = df.sort_values(['chrom','start','end']).reset_index(drop=True)
        
    return df

Overlap

In this chapter we characterize the performance of the key function, bioframe.overlap. We show that the speed depends on:

• the number of intervals

• number of intersections (or density of intervals)

• type of overlap (inner, outer, left)

• dtype of chromosomes

vs number of intervals

timings = {}
for n in [1e2, 1e3, 1e4, 1e5, 1e6]:
    df = make_random_intervals(n=n, n_chroms=1)
    df2 = make_random_intervals(n=n, n_chroms=1)
    timings[n] = %timeit -o -r 1 bioframe.overlap(df, df2)

4.92 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
7.13 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
42.3 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
448 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
6.95 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

plt.loglog(
    list(timings.keys()),
    list([r.average for r in timings.values()]),
    'o-',
)
plt.xlabel('N intervals')
plt.ylabel('time, seconds')
plt.gca().set_aspect(1.0)
plt.grid()

vs total number of intersections

Note that not only the number of intervals, but also the density of intervals determines the performance of overlap.

timings = {}
n_intersections = {}
n = 1e4
for avg_interval_len in [3, 1e1, 3e1, 1e2, 3e2]:
    df = make_random_intervals(n=n, n_chroms=1, max_length=avg_interval_len*2)
    df2 = make_random_intervals(n=n, n_chroms=1, max_length=avg_interval_len*2)
    timings[avg_interval_len] = %timeit -o -r 1 bioframe.overlap(df, df2)
    n_intersections[avg_interval_len] = bioframe.overlap(df, df2).shape[0]

22.7 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
45.5 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
163 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
611 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
2.51 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

plt.loglog(
    list(n_intersections.values()),
    list([r.average for r in timings.values()]),
    'o-',
)
plt.xlabel('N intersections')
plt.ylabel('time, seconds')
plt.gca().set_aspect(1.0)
plt.grid()

vs number of chromosomes

If we consider a genome of the same length, divided into more chromosomes, the timing is relatively unaffected.

timings = {}
n_intersections = {}
n = 1e5
for n_chroms in [1, 3, 10, 30, 100, 300, 1000]:
    df = make_random_intervals(n, n_chroms)
    df2 = make_random_intervals(n, n_chroms)
    timings[n_chroms] = %timeit -o -r 1 bioframe.overlap(df, df2)
    n_intersections[n_chroms] = bioframe.overlap(df, df2).shape[0]

443 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
456 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
414 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
434 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
451 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
409 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
443 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

Note this test preserves the number of intersections, which is likely why performance remains similar over the considered range.

n_intersections

{1: 810572,
 3: 810871,
 10: 809463,
 30: 815322,
 100: 808166,
 300: 802130,
 1000: 787235}

plt.loglog(
    list(timings.keys()),
    list([r.average for r in timings.values()]),
    'o-',
)
plt.ylim([1e-1, 10])
plt.xlabel('# chromosomes')
plt.ylabel('time, seconds')
# plt.gca().set_aspect(1.0)
plt.grid()

vs other parameters: join type, sorted or categorical inputs

Note that default for overlap: how='left', keep_order=True, and the returned dataframe is sorted after the overlaps have been ascertained. Also note that keep_order=True is only a valid argument for how='left' as the order is not well-defined for inner or outer overlaps.

df = make_random_intervals()
df2 = make_random_intervals()
%timeit -r 1 bioframe.overlap(df, df2)
%timeit -r 1 bioframe.overlap(df, df2, how='left', keep_order=False)

418 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
274 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

df = make_random_intervals()
df2 = make_random_intervals()

%timeit -r 1 bioframe.overlap(df, df2, how='outer')
%timeit -r 1 bioframe.overlap(df, df2, how='inner')
%timeit -r 1 bioframe.overlap(df, df2, how='left', keep_order=False)

329 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
151 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
247 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

Note below that detection of overlaps takes a relatively small fraction of the execution time. The majority of the time the user-facing function spends on formatting the output table.

df = make_random_intervals()
df2 = make_random_intervals()

%timeit -r 1 bioframe.overlap(df, df2)
%timeit -r 1 bioframe.overlap(df, df2, how='inner')
%timeit -r 1 bioframe.ops._overlap_intidxs(df, df2)
%timeit -r 1 bioframe.ops._overlap_intidxs(df, df2, how='inner')

449 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
148 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
61 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
62.4 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)

Note that sorting inputs provides a moderate speedup, as well as storing chromosomes as categoricals

print('Default inputs (outer/inner joins):')
df = make_random_intervals()
df2 = make_random_intervals()

%timeit -r 1 bioframe.overlap(df, df2)
%timeit -r 1 bioframe.overlap(df, df2, how='inner')

print('Sorted inputs (outer/inner joins):')
df_sorted = make_random_intervals(sort=True)
df2_sorted = make_random_intervals(sort=True)

%timeit -r 1 bioframe.overlap(df_sorted, df2_sorted)
%timeit -r 1 bioframe.overlap(df_sorted, df2_sorted, how='inner')

print('Categorical chromosomes (outer/inner joins):')
df_cat = make_random_intervals(categorical_chroms=True)
df2_cat = make_random_intervals(categorical_chroms=True)

%timeit -r 1 bioframe.overlap(df_cat, df2_cat)
%timeit -r 1 bioframe.overlap(df_cat, df2_cat, how='inner')

Default inputs (outer/inner joins):
440 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
149 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
Sorted inputs (outer/inner joins):
331 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
137 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
Categorical chromosomes (outer/inner joins):
333 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
90 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)

Vs Pyranges (and, optionally, pybedtools)

Default arguments

The core intersection function of PyRanges is faster, since PyRanges object splits intervals by chromosomes at the object construction stage

def df2pr(df):
    return pyranges.PyRanges(
    chromosomes=df.chrom,
    starts=df.start,
    ends=df.end,
    )

timings_bf = {}
timings_pr = {}
for n in [1e2, 1e3, 1e4, 1e5, 1e6, 3e6]:
    df = make_random_intervals(n=n, n_chroms=1)
    df2 = make_random_intervals(n=n, n_chroms=1)
    pr = df2pr(df)
    pr2 = df2pr(df2)
    timings_bf[n] = %timeit -o -r 1 bioframe.overlap(df, df2,how='inner')
    timings_pr[n] = %timeit -o -r 1 pr.join(pr2)
    

2.36 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
1.49 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
2.94 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
1.9 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
10.8 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
6.63 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
128 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
69.4 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
2.35 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
1.16 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
7.97 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
4.75 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

plt.loglog(
    list(timings_bf.keys()),
    list([r.average for r in timings_bf.values()]),
    'o-',
    label='bioframe'
)
plt.loglog(
    list(timings_pr.keys()),
    list([r.average for r in timings_pr.values()]),
    'o-',
    label='pyranges'
)

plt.gca().set(
    xlabel='N intervals',
    ylabel='time, seconds',
    aspect=1.0,
    xticks=10**np.arange(2,6.1)
)
plt.grid()
plt.legend()

<matplotlib.legend.Legend at 0x7fe3556e94b0>

With roundtrips to dataframes

Note that pyranges performs useful calculations at the stage of creating a PyRanges object. Thus a direct comparison for one-off operations on pandas DataFrames between bioframe and pyranges should take this step into account. This roundrip is handled by pyranges_intersect_dfs below.

def pyranges_intersect_dfs(df, df2):
    return df2pr(df).intersect(df2pr(df2)).as_df()

if include_pybedtools:
    def pybedtools_intersect_dfs(bed1, bed2):
        return bed1.intersect(bed2).to_dataframe()

timings_bf = {}
timings_pr = {}
if include_pybedtools:
    timings_pb = {}
    
for n in [1e2, 1e3, 1e4, 1e5, 1e6, 3e6]:
    df = make_random_intervals(n=n, n_chroms=1)
    df2 = make_random_intervals(n=n, n_chroms=1)
    timings_bf[n] = %timeit -o -r 1 bioframe.overlap(df, df2, how='inner')
    timings_pr[n] = %timeit -o -r 1 pyranges_intersect_dfs(df, df2)
    if include_pybedtools:
        bed1 = pybedtools.BedTool.from_dataframe(df)
        bed2 = pybedtools.BedTool.from_dataframe(df2)
        timings_pb[n] = %timeit -o -r 1 pybedtools_intersect_dfs(bed1, bed2)

2.28 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
3.9 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
3.02 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
4.64 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
11 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
11 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
125 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
87.4 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
2.15 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
1.44 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
7.98 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
5.23 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

plt.loglog(
    list(timings_bf.keys()),
    list([r.average for r in timings_bf.values()]),
    'o-',
    label='bioframe'
)
plt.loglog(
    list(timings_pr.keys()),
    list([r.average for r in timings_pr.values()]),
    'o-',
    label='pyranges'
)
if include_pybedtools:
    plt.loglog(
        list(timings_pb.keys()),
        list([r.average for r in timings_pb.values()]),
        'o-',
        label='pybedtools'
    )
plt.gca().set(
    xlabel='N intervals',
    ylabel='time, seconds',
    aspect=1.0
)
plt.grid()
plt.legend()

<matplotlib.legend.Legend at 0x7fe355f4d420>

Memory usage

from memory_profiler import memory_usage
import time

def sleep_before_after(func, sleep_sec=0.5):
    """ 
    Wrapper that allows to report background interpreter's memory consumption 
    for the first 5 time intervals (if increment is 0.1 abd sleep_sec=0.5):
    https://github.com/pythonprofilers/memory_profiler#api 
    """
    def _f(*args, **kwargs):
        time.sleep(sleep_sec)
        func(*args, **kwargs)
        time.sleep(sleep_sec)
    return _f

mem_usage_bf = {}
mem_usage_pr = {}
if include_pybedtools:
    mem_usage_pb = {}

for n in [1e2, 1e3, 1e4, 1e5, 1e6, 3e6]:
    df = make_random_intervals(n=n, n_chroms=1)
    df2 = make_random_intervals(n=n, n_chroms=1)
    mem_usage_bf[n] = memory_usage(
        (sleep_before_after(bioframe.overlap), (df, df2), dict( how='inner')), 
        backend='psutil_pss', 
        include_children=True,
        interval=0.1)
    mem_usage_pr[n] = memory_usage(
        (sleep_before_after(pyranges_intersect_dfs), (df, df2), dict()), 
        backend='psutil_pss', 
        include_children=True,
        interval=0.1)
    if include_pybedtools:
        bed1 = pybedtools.BedTool.from_dataframe(df)
        bed2 = pybedtools.BedTool.from_dataframe(df2)
        mem_usage_pb[n] = memory_usage(
            (sleep_before_after(pybedtools_intersect_dfs), (bed1, bed2), dict()), 
            backend='psutil_pss', 
            include_children=True,
            interval=0.1)
    

# Note that r[4] is the background memory usage of Python interpreter, 
# and max(r) is the maximum memory usage (that must be from the bioframe/pyranges functions)
plt.figure(figsize=(8,6))
plt.loglog(
    list(mem_usage_bf.keys()),
    list([max(r) - r[4] for r in mem_usage_bf.values()]),
    'o-',
    label='bioframe'
)

plt.loglog(
    list(mem_usage_pr.keys()),
    list([max(r) - r[4] for r in mem_usage_pr.values()]),
    'o-',
    label='pyranges'
)

if include_pybedtools:
    plt.loglog(
        list(mem_usage_pb.keys()),
        list([max(r) - r[4] for r in mem_usage_pb.values()]),
        'o-',
        label='pybedtools'
    )

plt.gca().set(
    xlabel='N intervals',
    ylabel='Memory usage, Mb',
    aspect=1.0
)
plt.grid()
plt.legend()

<matplotlib.legend.Legend at 0x7fe38f4014b0>

print('Bioframe dtypes:')
display(df.dtypes)
print()

print('Pyranges dtypes:')
display(df2pr(df).dtypes)

if include_pybedtools:
    print('Pybedtools dtypes:')
    bed1 = pybedtools.BedTool.from_dataframe(df)
    display(bed1.to_dataframe().dtypes)

Bioframe dtypes:

chrom    object
start     int64
end       int64
dtype: object

Pyranges dtypes:

Chromosome    category
Start            int64
End              int64
dtype: object

### Combined performance figure.

fig, axs = plt.subplot_mosaic(
    'AAA.BBB',
    figsize=(9.0,4))

plt.sca(axs['A'])                   

plt.text(-0.25, 1.0, 'A', horizontalalignment='center',
    verticalalignment='center', transform=plt.gca().transAxes,
        fontsize=19)

plt.loglog(
    list(timings_bf.keys()),
    list([r.average for r in timings_bf.values()]),
    'o-',
    color='k',
    label='bioframe'
)
plt.loglog(
    list(timings_pr.keys()),
    list([r.average for r in timings_pr.values()]),
    'o-',
    color='gray',
    label='pyranges'
)
if include_pybedtools:
    plt.loglog(
        list(timings_pb.keys()),
        list([r.average for r in timings_pb.values()]),
        'o-',
        color='lightgray',
        label='pybedtools'
)

plt.gca().set(
    xlabel='N intervals',
    ylabel='time, s',
    aspect=1.0,
    xticks=10**np.arange(2,6.1),
    yticks=10**np.arange(-3,0.1),

)

plt.grid()
plt.legend()

plt.sca(axs['B'])
plt.text(-0.33, 1.0, 'B', horizontalalignment='center',
    verticalalignment='center', transform=plt.gca().transAxes,
        fontsize=19)
plt.loglog(
    list(mem_usage_bf.keys()),
    list([max(r) - r[4] for r in mem_usage_bf.values()]),
    'o-',
    color='k',
    label='bioframe'
)

plt.loglog(
    list(mem_usage_pr.keys()),
    list([max(r) - r[4] for r in mem_usage_pr.values()]),
    'o-',
    color='gray',
    label='pyranges'
)
if include_pybedtools:
    plt.loglog(
        list(mem_usage_pb.keys()),
        list([max(r) - r[4] for r in mem_usage_pb.values()]),
        'o-',
        color='lightgray',
        label='pybedtools'
    )

plt.gca().set(
    xlabel='N intervals',
    ylabel='Memory usage, Mb',
    aspect=1.0,
    xticks=10**np.arange(2,6.1),
)

plt.grid()
plt.legend()

<matplotlib.legend.Legend at 0x7fe3548d5120>

Slicing

timings_slicing_bf = {}
timings_slicing_pr = {}


for n in [1e2, 1e3, 1e4, 1e5, 1e6, 3e6]:
    df = make_random_intervals(n=n, n_chroms=1)
    timings_slicing_bf[n] = %timeit -o -r 1 bioframe.select(df, ('chr1', n//2, n//4*3))
    pr = df2pr(df)
    timings_slicing_pr[n] = %timeit -o -r 1 pr['chr1', n//2:n//4*3]
    

334 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
468 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
346 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
593 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
668 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
1.92 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)
3.54 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
18.1 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)
40.1 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
222 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
118 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)
804 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

plt.loglog(
    list(timings_slicing_bf.keys()),
    list([r.average for r in timings_bf.values()]),
    'o-',
    label='bioframe'
)

plt.loglog(
    list(timings_slicing_pr.keys()),
    list([r.average for r in timings_pr.values()]),
    'o-',
    label='pyranges'
)
plt.gca().set(
    xlabel='N intervals',
    ylabel='time, s',
    aspect=1.0
)
plt.grid()
plt.legend()

<matplotlib.legend.Legend at 0x7fe3174e49d0>

How do I

Obtain overlapping intervals with matching strandedness?

Use overlap with the on argument:

df = bf.overlap(df1, df2, on=[‘strand’])

Obtain overlapping intervals with opposite strandedness?

Overlap then filter pairs of opposite strandedness:

df = bf.overlap(df1, df2)
df = df.loc[df["strand"]!=df["strand_"]]

Obtain intervals that exceed 50% coverage by another set of intervals?

Coverage, then filter pairs by fractional coverage:

df = bf.coverage(df1, df2)
df = df[ ( df["coverage"] / (df["end"]-df["start"]) ) >=0.50]

Shift all intervals on the positive strand by 10bp?

Use pandas indexing:

df.loc[df.strand=="+",["start", "end"]] += 10

Obtain intervals overlapped by at least 2 intervals from another set?

Count overlaps, then filter:

df = bf.count_overlaps(df1, df2)
df = df[ df["count"] >= 2]

Find strand-specific downstream genomic features?

Use closest after filtering by strand, and passing the ignore_upsream=True argument.

bioframe.closest(df1.loc[df1['strand']=='+'], df2, ignore_upstream=True)

For gener, the upstream/downstream direction might be defined by the direction of transcription. Use direction_col='strand' to set up the direction:

bioframe.closest(df1, df2, ignore_upstream=True, direction_col='strand')

Drop non-autosomes from a bedframe?

Use pandas DataFrame.isin(values):

df[ ~df.chrom.isin(['chrX','chrY'])]

Definitions

Interval:

• An interval is a tuple of integers (start, end) with start <= end.

• Coordinates are assumed to be 0-based and intervals half-open (1-based ends) i.e. [start, end).

• An interval has a length equal to (end - start).

• A special case where start and end are the same, i.e. [X, X), is interpreted as a point (aka an empty interval, i.e. an edge between 1-bp bins). A point has zero length.

• Negative coordinates are permissible for both ends of an interval.

Properties of a pair of intervals:

• Two intervals can either overlap, or not. The overlap length = max(0, min(end1, end2) - max(start1, start2)). Empty intervals can have overlap length = 0.

• When two intervals overlap, the shorter of the two intervals is said to be contained in the longer one if the length of their overlap equals the length of the shorter interval. This property is often referred to as nestedness, but we use the term “contained” as it is less ambiguous when describing the relationship of sets of intervals to one interval.

• If two intervals do not overlap, they have a distance = max(0, max(start1, start2) - min(end1, end2)).

• If two intervals have overlap=0 and distance=0, they are said to be abutting.

Scaffold:

• A chromosome, contig or, more generally, a scaffold is an interval defined by a unique string and has a length>=0, with start=0 and end=length, implicitly defining an interval [0, length).

Genome assembly:

• The complete set of scaffolds associated with a genome is called an assembly (e.g. defined by the reference sequence from NCBI, etc.).

Genomic interval:

• A genomic interval is an interval with an associated scaffold, or chromosome, defined by a string, i.e. a triple (chrom, start, end).

• Genomic intervals on different scaffolds never overlap and do not have a defined distance.

• Genomic intervals can extend beyond their associated scaffold (e.g. with negative values or values greater than the scaffold length), as this can be useful in downstream applications. If they do, they are not contained by their associated scaffold.

• A base-pair is a special case of a genomic interval with length=1, i.e. (chrom, start, start+1)

• strand is an (optional) property of a genomic interval which specifies an interval’s orientation on its scaffold. Note start and end are still defined with respect to the scaffold’s reference orientation (positive strand), even if the interval lies on the negative strand. Intervals on different strands can either be allowed to overlap or not.

View (i.e. a set of Genomic Regions):

• A genomic view is an ordered set of non-overlapping genomic intervals each having a unique name defined by a string. Individual named intervals in a view are regions, defined by a quadruple, e.g. (chrom, start, end, name).

• A view thus specifies a unified 1D coordinate system, i.e. a projection of multiple genomic regions onto a single axis.

• We define views separately from the scaffolds that make up a genome assembly, as a set of more constrained and ordered genomic regions are often useful for downstream analysis and visualization.

• An assembly is a special case of a view, where the individual regions correspond to the assembly’s entire scaffolds.

Associating genomic intervals with views

• Similarly to how genomic intervals are associated with a scaffold, they can also be associated with a region from a view with an additional string, making a quadruple (chrom, start, end, view_region). This string must be cataloged in the view, i.e. it must match the name of a region in the view. Typically the interval would be contained in its associated view region, or, at the minimum, have a greater overlap with that region than other view regions.

• If each interval in a set is contained in their associated view region, the set is contained in the view.

• A set of intervals covers a view if each region in the view is contained by the union of its associated intervals. Conversely, if a set does not cover all of view regions, the interval set will have gaps relative to that view (stretches of bases not covered by an interval).

Properties of sets of genomic intervals:

• A set of genomic intervals may have overlaps or not. If it does not, it is said to be overlap-free.

• A set of genomic intervals is tiling if it: (i) covers the associated view, (ii) is contained in that view, and (iii) is overlap-free. Equivalently, a tiling set of intervals (a) has an initial interval that begins at the start of each region and (b) a final interval that terminates at the end of each region, and (c) every base pair is associated with a unique interval.

Specifications

BedFrame (i.e. genomic intervals stored in a pandas dataframe):

• In a BedFrame, three required columns specify the set of genomic intervals (default column names = (‘chrom’, ‘start’, ‘end’)).

• Other reserved but not required column names: (‘strand’, ‘name’, ‘view_region’).

  • entries in column ‘name’ are expected to be unique

  • ‘view_region’ is expected to point to an associated region in a view with a matching name

  • ‘strand’ is expected to be encoded with strings (‘+’, ‘-’, ‘.’).

• Additional columns are allowed: ‘zodiac_sign’, ‘soundcloud’, ‘twitter_name’, etc.

• Repeated intervals are allowed.

• The native pandas DataFrame index is not intended to be used as an immutable lookup table for genomic intervals in BedFrame. This is because many common genomic interval operations change the number of intervals stored in a BedFrame.

• Two useful sorting schemes for BedFrames are:

  • scaffold-sorted: on (chrom, start, end), where chrom is sorted lexicographically.

  • view-sorted: on (view_region, start, end) where view_region is sorted by order in the view.

• Null values are allowed, but only as pd.NA (using np.nan is discouraged as it results in unwanted type re-casting).

• Note if no ‘view_region’ is assigned to a genomic interval, then ‘chrom’ implicitly defines an associated region

• Note the BedFrame specification is a natural extension of the BED format ( ​​https://samtools.github.io/hts-specs/BEDv1.pdf ) for pandas DataFrames.

ViewFrames (a genomic view stored in a pandas dataframe)

• BedFrame where:

  • intervals are non-overlapping

  • “name” column is mandatory and contains a set of unique strings.

• Note that a ViewFrame can potentially be indexed by the name column to serve as a lookup table. This functionality is currently not implemented, because within the current Pandas implementation indexing by a column removes the column from the table.

• Note that views can be defined by:

  • dictionary of string:ints (start=0 assumed) or string:tuples (start,end)

  • pandas series of chromsizes (start=0, name=chrom)

How to: assign TF Motifs to ChIP-seq peaks

This tutorial demonstrates one way to assign CTCF motifs to CTCF ChIP-seq peaks using bioframe.

import bioframe
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt

base_dir = '/tmp/bioframe_tutorial_data/'
assembly = 'GRCh38'

Load CTCF ChIP-seq peaks for HFF from ENCODE

This approach makes use of the narrowPeak schema for bioframe.read_table .

ctcf_peaks = bioframe.read_table("https://www.encodeproject.org/files/ENCFF401MQL/@@download/ENCFF401MQL.bed.gz", schema='narrowPeak')
ctcf_peaks[0:5]

	chrom	start	end	name	score	strand	fc	-log10p	-log10q	relSummit
0	chr19	48309541	48309911	.	1000	.	5.04924	-1.0	0.00438	185
1	chr4	130563716	130564086	.	993	.	5.05052	-1.0	0.00432	185
2	chr1	200622507	200622877	.	591	.	5.05489	-1.0	0.00400	185
3	chr5	112848447	112848817	.	869	.	5.05841	-1.0	0.00441	185
4	chr1	145960616	145960986	.	575	.	5.05955	-1.0	0.00439	185

Get CTCF motifs from JASPAR

### CTCF motif: http://jaspar.genereg.net/matrix/MA0139.1/
jaspar_url = 'http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/hg38/'
jaspar_motif_file = 'MA0139.1.tsv.gz'
ctcf_motifs = bioframe.read_table(jaspar_url+jaspar_motif_file,schema='jaspar',skiprows=1)    
ctcf_motifs[0:4]

	chrom	start	end	name	score	pval	strand
0	chr1	11163	11182	CTCF	811	406	-
1	chr1	11222	11241	CTCF	959	804	-
2	chr1	11280	11299	CTCF	939	728	-
3	chr1	11339	11358	CTCF	837	455	-

Overlap peaks & motifs

df_peaks_motifs = bioframe.overlap(ctcf_peaks,ctcf_motifs, suffixes=('_1','_2'), return_index=True)

There are often multiple motifs overlapping one ChIP-seq peak, and a substantial number of peaks without motifs:

# note that counting motifs per peak can also be handled directly with bioframe.count_overlaps
# but since we re-use df_peaks_motifs below we instead use the pandas operations directly
motifs_per_peak = df_peaks_motifs.groupby(["index_1"])["index_2"].count().values

plt.hist(motifs_per_peak,np.arange(0,np.max(motifs_per_peak)))
plt.xlabel('number of overlapping motifs per peak')
plt.ylabel('number of peaks')
plt.semilogy();

print(f'fraction of peaks without motifs {np.round(np.sum(motifs_per_peak==0)/len(motifs_per_peak),2)}')

fraction of peaks without motifs 0.14

Assign the strongest motif to each peak

# since idxmax does not currently take NA, fill with -1
df_peaks_motifs['pval_2'] = df_peaks_motifs['pval_2'].fillna(-1) 
idxmax_peaks_motifs = df_peaks_motifs.groupby(["chrom_1", "start_1","end_1"])["pval_2"].idxmax().values
df_peaks_maxmotif = df_peaks_motifs.loc[idxmax_peaks_motifs]
df_peaks_maxmotif['pval_2'].replace(-1,np.nan,inplace=True)

/tmp/ipykernel_730/2992559030.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  df_peaks_maxmotif['pval_2'].replace(-1,np.nan,inplace=True)

stronger peaks tend to have stronger motifs:

plt.rcParams['font.size']=12
df_peaks_maxmotif['fc_1']  = df_peaks_maxmotif['fc_1'].values.astype('float')
plt.scatter(df_peaks_maxmotif['fc_1'].values, 
            df_peaks_maxmotif['pval_2'].values, 5, alpha=0.5,lw=0)
plt.xlabel('ENCODE CTCF peak strength, fc')
plt.ylabel('JASPAR CTCF motif strength \n (-log10 pval *100)')
plt.title('corr: '+str(np.round(df_peaks_maxmotif['fc_1'].corr(df_peaks_maxmotif['pval_2']),2)));

We can also ask the reverse question: how many motifs overlap a ChIP-seq peak?

df_motifs_peaks = bioframe.overlap(ctcf_motifs,ctcf_peaks,how='left', suffixes=('_1','_2'))

m = df_motifs_peaks.sort_values('pval_1')
plt.plot( m['pval_1'].values[::-1] ,
    np.cumsum(pd.isnull(m['chrom_2'].values[::-1])==0)/np.arange(1,len(m)+1))
plt.xlabel('pval')
plt.ylabel('probability motif overlaps a peak');

filter peaks overlapping blacklisted regions

do any of our peaks overlap blacklisted genomic regions?

blacklist = bioframe.read_table('https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz',
                                schema='bed3')
blacklist[0:3]

	chrom	start	end
0	chr1	628903	635104
1	chr1	5850087	5850571
2	chr1	8909610	8910014

there appears to be a small spike in the number of peaks close to blacklist regions

closest_to_blacklist = bioframe.closest(ctcf_peaks,blacklist)
plt.hist(closest_to_blacklist['distance'].astype('Float64').astype('float'),np.arange(0,1e4,100));

to be safe, let’s remove anything +/- 1kb from a blacklisted region

# first let's select the columns we want for our final dataframe of peaks with motifs
df_peaks_maxmotif = df_peaks_maxmotif[
                            ['chrom_1','start_1','end_1','fc_1',
                             'chrom_2','start_2','end_2','pval_2','strand_2']]
#  then rename columns for convenience when subtracting
for i in df_peaks_maxmotif.keys():
    if '_1' in i: df_peaks_maxmotif.rename(columns={i:i.split('_')[0]},inplace=True)

# now subtract, expanding the blacklist by 1kb        
df_peaks_maxmotif_clean = bioframe.subtract(df_peaks_maxmotif,bioframe.expand(blacklist,1000))

there it is! we now have a dataframe containing positions of CTCF ChIP peaks, including the strongest motif underlying that peak, and after conservative filtering for proximity to blacklisted regions

df_peaks_maxmotif_clean.iloc[7:15]

	chrom	start	end	fc	chrom_2	start_2	end_2	pval_2	strand_2
7	chr9	124777413	124777783	5.06479	chr9	124777400	124777419	450.0	+
8	chr1	67701045	67701415	5.06708	None	<NA>	<NA>	NaN	None
9	chr10	119859586	119859956	5.08015	chr10	119859591	119859610	611.0	-
10	chr3	66816327	66816697	5.08233	chr3	66816332	66816351	741.0	-
11	chr16	50248791	50249161	5.08249	None	<NA>	<NA>	NaN	None
12	chr19	41431677	41432047	5.11060	chr19	41431802	41431821	477.0	+
13	chr4	131644839	131645209	5.11204	None	<NA>	<NA>	NaN	None
14	chr2	203239519	203239889	5.11817	None	<NA>	<NA>	NaN	None

How to: assign ChIP-seq peaks to genes

This tutorial demonstrates one way to assign CTCF ChIP-seq peaks to the nearest genes using bioframe.

import bioframe
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt

base_dir = '/tmp/bioframe_tutorial_data/'
assembly = 'hg38'

Load chromosome sizes

chromsizes = bioframe.fetch_chromsizes(assembly)
chromsizes.tail()

name
chr21     46709983
chr22     50818468
chrX     156040895
chrY      57227415
chrM         16569
Name: length, dtype: int64

chromosomes = bioframe.make_viewframe(chromsizes)

Load CTCF ChIP-seq peaks for HFF from ENCODE

This approach makes use of the narrowPeak schema for bioframe.read_table .

ctcf_peaks = bioframe.read_table("https://www.encodeproject.org/files/ENCFF401MQL/@@download/ENCFF401MQL.bed.gz", schema='narrowPeak')
ctcf_peaks.head()

	chrom	start	end	name	score	strand	fc	-log10p	-log10q	relSummit
0	chr19	48309541	48309911	.	1000	.	5.04924	-1.0	0.00438	185
1	chr4	130563716	130564086	.	993	.	5.05052	-1.0	0.00432	185
2	chr1	200622507	200622877	.	591	.	5.05489	-1.0	0.00400	185
3	chr5	112848447	112848817	.	869	.	5.05841	-1.0	0.00441	185
4	chr1	145960616	145960986	.	575	.	5.05955	-1.0	0.00439	185

# Filter for selected chromosomes:
ctcf_peaks = bioframe.overlap(ctcf_peaks, chromosomes).dropna(subset=['name_'])[ctcf_peaks.columns]

Get list of genes from UCSC

UCSC genes are stored in .gtf format.

genes_url = 'https://hgdownload.soe.ucsc.edu/goldenpath/hg38/bigZips/genes/hg38.ensGene.gtf.gz'
genes = bioframe.read_table(genes_url, schema='gtf').query('feature=="CDS"')

genes.head() 

## Note this functions to parse the attributes of the genes:
# import bioframe.sandbox.gtf_io
# genes_attr = bioframe.sandbox.gtf_io.parse_gtf_attributes(genes['attributes'])

	chrom	source	feature	start	end	score	strand	frame	attributes
47	chr1	ensGene	CDS	69091	70005	.	+	0	gene_id "ENSG00000186092"; transcript_id "ENST...
112	chr1	ensGene	CDS	182709	182746	.	+	0	gene_id "ENSG00000279928"; transcript_id "ENST...
114	chr1	ensGene	CDS	183114	183240	.	+	1	gene_id "ENSG00000279928"; transcript_id "ENST...
116	chr1	ensGene	CDS	183922	184155	.	+	0	gene_id "ENSG00000279928"; transcript_id "ENST...
122	chr1	ensGene	CDS	185220	185350	.	-	2	gene_id "ENSG00000279457"; transcript_id "ENST...

# Filter for selected chromosomes:
genes = bioframe.overlap(genes, chromosomes).dropna(subset=['name_'])[genes.columns]

Assign each peak to the gene

Here, we want to assign each peak (feature) to a gene (input table).

peaks_closest = bioframe.closest(genes, ctcf_peaks)

# Plot the distribution of distances from peaks to genes: 
plt.hist( peaks_closest['distance'], np.arange(0, 1e3, 10));
plt.xlim( [0, 1e3] )

(0.0, 1000.0)

Ignore upstream/downstream peaks from genes (strand-indifferent version)

Sometimes you may want to ignore all the CTCFs upstream from the genes.

By default, bioframe.overlap does not know the orintation of the genes, and thus assumes that the upstream/downstream is defined by the genomic coordinate (upstream is the direction towards the smaller coordinate):

peaks_closest_upstream_nodir = bioframe.closest(genes, ctcf_peaks, 
    ignore_overlaps=False,
    ignore_upstream=False,
    ignore_downstream=True,
    direction_col=None)

peaks_closest_downstream_nodir = bioframe.closest(genes, ctcf_peaks, 
    ignore_overlaps=False,
    ignore_upstream=True,
    ignore_downstream=False,
    direction_col=None)

Note that distribution did not change much, and upstream and downstream distances are very similar:

plt.hist( peaks_closest_upstream_nodir['distance'], np.arange(0, 1e4, 100), alpha=0.5, label="upstream");
plt.hist( peaks_closest_downstream_nodir['distance'], np.arange(0, 1e4, 100), alpha=0.5, label="downstream");
plt.xlim( [0, 1e4] )
plt.legend()

<matplotlib.legend.Legend at 0x7f0eaf129420>

Ignore upstream/downstream peaks from genes (strand-aware version)

More biologically relevant approach will be to define upstream/downstream by strand of the gene. CTCF upstream of transcription start site might play different role than CTCF after transcription end site.

bioframe.closest has the parameter direction_col to control for that:

# Note that "strand" here is the column name in genes table:
peaks_closest_upstream_dir = bioframe.closest(genes, ctcf_peaks, 
    ignore_overlaps=False,
    ignore_upstream=False,
    ignore_downstream=True,
    direction_col='strand')

peaks_closest_downstream_dir = bioframe.closest(genes, ctcf_peaks, 
    ignore_overlaps=False,
    ignore_upstream=True,
    ignore_downstream=False,
    direction_col='strand')

plt.hist( peaks_closest_upstream_dir['distance'], np.arange(0, 1e4, 100), alpha=0.5, label="upstream");
plt.hist( peaks_closest_downstream_dir['distance'], np.arange(0, 1e4, 100), alpha=0.5, label="downstream");
plt.xlim( [0, 1e4] )
plt.legend()

<matplotlib.legend.Legend at 0x7f0eb839b610>

CTCF peaks upstream of the genes are more enriched at short distances to TSS, if we take the strand into account.

Construction

Functions:

from_any(regions[, fill_null, name_col, cols])	Attempts to make a genomic interval dataframe with columns [chr, start, end, name_col] from a variety of input types.
from_dict(regions[, cols])	Makes a dataframe from a dictionary of {str,int} pairs, interpreted as chromosome names.
make_viewframe(regions[, check_bounds, ...])	Makes and validates a dataframe view_df out of regions.
sanitize_bedframe(df1[, recast_dtypes, ...])	Attempts to clean a genomic interval dataframe to be a valid bedframe.

from_any(regions, fill_null=False, name_col='name', cols=None)

Attempts to make a genomic interval dataframe with columns [chr, start, end, name_col] from a variety of input types.

Parameters:

• regions (supported input) –

  Currently supported inputs:

  • dataframe

  • series of UCSC strings

  • dictionary of {str:int} key value pairs

  • pandas series where the index is interpreted as chromosomes and values are interpreted as end

  • list of tuples or lists, either [(chrom,start,end)] or [(chrom,start,end,name)]

  • tuple of tuples or lists, either [(chrom,start,end)] or [(chrom,start,end,name)]

• fill_null (False or dictionary) – Accepts a dictionary of {str:int} pairs, interpreted as chromosome sizes. Kept or backwards compatibility. Default False.

• name_col (str) – Column name. Only used if 4 column list is provided. Default “name”.

• cols ((str,str,str)) – Names for dataframe columns. Default None sets them with get_default_colnames().

Returns:

out_df

Return type:

dataframe

from_dict(regions, cols=None)

Makes a dataframe from a dictionary of {str,int} pairs, interpreted as chromosome names.

Note that {str,(int,int)} dictionaries of tuples are no longer supported!

Parameters:

• regions (dict) –

• name_col (str) – Default ‘name’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df

Return type:

pandas.DataFrame

make_viewframe(regions, check_bounds=None, name_style=None, view_name_col='name', cols=None)

Makes and validates a dataframe view_df out of regions.

Parameters:

• regions (supported input type) –

  Currently supported input types:

  • a dictionary where keys are strings and values are integers {str:int}, specifying regions (chrom, 0, end, chrom)

  • a pandas series of chromosomes lengths with index specifying region names

  • a list of tuples [(chrom,start,end), …] or [(chrom,start,end,name), …]

  • a pandas DataFrame, skips to validation step

• name_style (None or "ucsc") – If None and no column view_name_col, propagate values from cols[0] If “ucsc” and no column view_name_col, create UCSC style names

• check_bounds (None, or chromosome sizes provided as any of valid formats above) – Optional, if provided checks if regions in the view are contained by regions supplied in check_bounds, typically provided as a series of chromosome sizes. Default None.

• view_name_col (str) – Specifies column name of the view regions. Default ‘name’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

view_df

Return type:

dataframe satisfying properties of a view

sanitize_bedframe(df1, recast_dtypes=True, drop_null=False, start_exceed_end_action=None, cols=None)

Attempts to clean a genomic interval dataframe to be a valid bedframe.

Parameters:

• df1 (pandas.DataFrame) –

• recast_dtypes (bool) – Whether to attempt to recast column dtypes to pandas nullable dtypes.

• drop_null (bool) – Drops rows with pd.NA. Default False.

• start_exceed_end_action (str or None) –

  Options: ‘flip’ or ‘drop’ or None. Default None.

  • If ‘flip’, attempts to sanitize by flipping intervals with start>end.

  • If ‘drop’ attempts to sanitize dropping intervals with start>end.

  • If None, does not alter these intervals if present.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

out_df – Sanitized dataframe satisfying the properties of a bedframe.

Return type:

pandas.DataFrame

Notes

The option start_exceed_end_action='flip' may be useful for gff files with strand information but starts > ends.

Validation

Functions:

is_bedframe(df[, raise_errors, cols])	Checks that required bedframe properties are satisfied for dataframe df.
is_cataloged(df, view_df[, raise_errors, ...])	Tests if all region names in df[df_view_col] are present in view_df[view_name_col].
is_contained(df, view_df[, raise_errors, ...])	Tests if all genomic intervals in a bioframe df are cataloged and do not extend beyond their associated region in the view view_df.
is_covering(df, view_df[, view_name_col, ...])	Tests if a view view_df is covered by the set of genomic intervals in the bedframe df.
is_overlapping(df[, cols])	Tests if any genomic intervals in a bioframe df overlap.
is_sorted(df[, view_df, reset_index, ...])	Tests if a bedframe is changed by sorting.
is_tiling(df, view_df[, raise_errors, ...])	Tests if a view view_df is tiled by the set of genomic intervals in the bedframe df.
is_viewframe(region_df[, raise_errors, ...])	Checks that region_df is a valid viewFrame.

is_bedframe(df, raise_errors=False, cols=None)

Checks that required bedframe properties are satisfied for dataframe df.

This includes:

• chrom, start, end columns

• columns have valid dtypes

• for each interval, if any of chrom, start, end are null, then all are

  null

• all starts < ends.

Parameters:

• df (pandas.DataFrame) –

• raise_errors (bool, optional [default: False]) – If True, raises errors instead of returning a boolean False for invalid properties.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_bedframe

Return type:

bool

Notes

Valid dtypes for chrom are object, string, or categorical. Valid dtypes for start and end are int/Int64Dtype.

is_cataloged(df, view_df, raise_errors=False, df_view_col='view_region', view_name_col='name')

Tests if all region names in df[df_view_col] are present in view_df[view_name_col].

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame) –

• raise_errors (bool) – If True, raises errors instead of returning a boolean False for invalid properties. Default False.

• df_view_col (str) – Name of column from df that indicates region in view.

• view_name_col (str) – Name of column from view that specifies region name.

Returns:

is_cataloged

Return type:

bool

Notes

Does not check if names in view_df[view_name_col] are unique.

is_contained(df, view_df, raise_errors=False, df_view_col=None, view_name_col='name', cols=None, cols_view=None)

Tests if all genomic intervals in a bioframe df are cataloged and do not extend beyond their associated region in the view view_df.

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame) – Valid viewframe.

• raise_errors (bool) – If True, raises errors instead of returning a boolean False for invalid properties. Default False.

• df_view_col – Column from df used to associate interviews with view regions. Default view_region.

• view_name_col – Column from view_df with view region names. Default name.

• cols ((str, str, str)) – Column names for chrom, start, end in df.

• cols_view ((str, str, str)) – Column names for chrom, start, end in view_df.

Returns:

is_contained

Return type:

bool

is_covering(df, view_df, view_name_col='name', cols=None, cols_view=None)

Tests if a view view_df is covered by the set of genomic intervals in the bedframe df.

This test is true if complement(df,view_df) is empty. Also note this test ignores regions assigned to intervals in df since regions are re-assigned in bioframe.ops.complement().

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame) – Valid viewFrame.

• view_name_col – Column from view_df with view region names. Default name.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in view_df, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_covering

Return type:

bool

is_overlapping(df, cols=None)

Tests if any genomic intervals in a bioframe df overlap.

Also see bioframe.ops.merge().

Parameters:

• df (pandas.DataFrame) –

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_overlapping

Return type:

bool

is_sorted(df, view_df=None, reset_index=True, df_view_col=None, view_name_col='name', cols=None, cols_view=None)

Tests if a bedframe is changed by sorting.

Also see bioframe.ops.sort_bedframe().

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame | dict-like) – Optional view to pass to sort_bedframe. When it is dict-like :func:’bioframe.make_viewframe’ will be used to convert to viewframe. If view_df is not provided df is assumed to be sorted by chrom and start.

• reset_index (bool) – Optional argument to pass to sort_bedframe.

• df_view_col (None | str) – Name of column from df that indicates region in view. If None, :func:’bioframe.assign_view’ will be used to assign view regions. Default None.

• view_name_col (str) – Name of column from view that specifies unique region name.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in view_df, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_sorted

Return type:

bool

is_tiling(df, view_df, raise_errors=False, df_view_col='view_region', view_name_col='name', cols=None, cols_view=None)

Tests if a view view_df is tiled by the set of genomic intervals in the bedframe df.

This is true if:

• df is not overlapping

• df is covering view_df

• df is contained in view_df

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame) – valid viewFrame

• raise_errors (bool) – If True, raises errors instead of returning a boolean False for invalid properties. Default False.

• df_view_col (str) – Name of column from df that indicates region in view.

• view_name_col (str) – Name of column from view that specifies unique region name.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in view_df, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_tiling

Return type:

bool

is_viewframe(region_df, raise_errors=False, view_name_col='name', cols=None)

Checks that region_df is a valid viewFrame.

This includes:

• it satisfies requirements for a bedframe, including columns for (‘chrom’, ‘start’, ‘end’)

• it has an additional column, view_name_col, with default ‘name’

• it does not contain null values

• entries in the view_name_col are unique.

• intervals are non-overlapping

Parameters:

• region_df (pandas.DataFrame) – Dataframe of genomic intervals to be tested.

• raise_errors (bool) – If True, raises errors instead of returning a boolean False for invalid properties. Default False.

• view_name_col (str) – Specifies column name of the view regions. Default ‘name’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

is_viewframe

Return type:

bool

Interval operations

Functions:

assign_view(df, view_df[, drop_unassigned, ...])	Associates genomic intervals in bedframe df with regions in viewframe view_df, based on their largest overlap.
closest(df1[, df2, k, ignore_overlaps, ...])	For every interval in dataframe df1 find k closest genomic intervals in dataframe df2.
cluster(df[, min_dist, cols, on, ...])	Cluster overlapping intervals into groups.
complement(df[, view_df, view_name_col, ...])	Find genomic regions in a viewFrame 'view_df' that are not covered by any interval in the dataFrame 'df'.
count_overlaps(df1, df2[, suffixes, ...])	Count number of overlapping genomic intervals.
coverage(df1, df2[, suffixes, return_input, ...])	Quantify the coverage of intervals from 'df1' by intervals from 'df2'.
expand(df[, pad, scale, side, cols])	Expand each interval by an amount specified with pad.
merge(df[, min_dist, cols, on])	Merge overlapping intervals.
overlap(df1, df2[, how, return_input, ...])	Find pairs of overlapping genomic intervals.
select(df, region[, cols])	Return all genomic intervals in a dataframe that overlap a genomic region.
select_indices(df, region[, cols])	Return integer indices of all genomic intervals that overlap a query range.
select_labels(df, region[, cols])	Return pandas Index labels of all genomic intervals that overlap a query range.
select_mask(df, region[, cols])	Return boolean mask for all genomic intervals that overlap a query range.
setdiff(df1, df2[, cols1, cols2, on])	Generate a new dataframe of genomic intervals by removing any interval from the first dataframe that overlaps an interval from the second dataframe.
sort_bedframe(df[, view_df, reset_index, ...])	Sorts a bedframe 'df'.
subtract(df1, df2[, return_index, suffixes, ...])	Generate a new set of genomic intervals by subtracting the second set of genomic intervals from the first.
trim(df[, view_df, df_view_col, ...])	Trim each interval to fall within regions specified in the viewframe 'view_df'.

assign_view(df, view_df, drop_unassigned=False, df_view_col='view_region', view_name_col='name', cols=None, cols_view=None)

Associates genomic intervals in bedframe df with regions in viewframe view_df, based on their largest overlap.

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.DataFrame) – ViewFrame specifying region start and ends for assignment. Attempts to convert dictionary and pd.Series formats to viewFrames.

• drop_unassigned (bool) – If True, drop intervals in df that do not overlap a region in the view. Default False.

• df_view_col (str) – The column of df used to specify view regions. The associated region in view_df is then used for trimming. If no view_df is provided, uses the chrom column, df[cols[0]]. Default “view_region”.

• view_name_col (str) – Column of view_df with region names. Default ‘name’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in the view. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

• out_df (dataframe with an associated view region for each interval in)

• out_df[view_name_col].

Notes

Resets index.

closest(df1, df2=None, k=1, ignore_overlaps=False, ignore_upstream=False, ignore_downstream=False, direction_col=None, tie_breaking_col=None, return_input=True, return_index=False, return_distance=True, return_overlap=False, suffixes=('', '_'), cols1=None, cols2=None)

For every interval in dataframe df1 find k closest genomic intervals in dataframe df2.

Currently, we are not taking the feature strands into account for filtering. However, the strand can be used for definition of upstream/downstream of the feature (direction).

Note that, unless specified otherwise, overlapping intervals are considered as closest. When multiple intervals are located at the same distance, the ones with the lowest index in df2 are returned.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame. If df2 is None, find closest non-identical intervals within the same set.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame. If df2 is None, find closest non-identical intervals within the same set.

• k (int) – The number of the closest intervals to report.

• ignore_overlaps (bool) – If True, ignore overlapping intervals and return the closest non-overlapping interval.

• ignore_upstream (bool) – If True, ignore intervals in df2 that are upstream of intervals in df1, relative to the reference strand or the strand specified by direction_col.

• ignore_downstream (bool) – If True, ignore intervals in df2 that are downstream of intervals in df1, relative to the reference strand or the strand specified by direction_col.

• direction_col (str) – Name of direction column that will set upstream/downstream orientation for each feature. The column should contain bioframe-compliant strand values (“+”, “-”, “.”).

• tie_breaking_col (str) – A column in df2 to use for breaking ties when multiple intervals are located at the same distance. Intervals with lower values will be selected.

• return_input (bool) – If True, return input

• return_index (bool) – If True, return indices

• return_distance (bool) – If True, return distances. Returns zero for overlaps.

• return_overlap (bool) – If True, return columns: ‘have_overlap’, ‘overlap_start’, and ‘overlap_end’. Fills df_closest[‘overlap_start’] and df[‘overlap_end’] with None if non-overlapping. Default False.

• suffixes ((str, str)) – The suffixes for the columns of the two sets.

• cols1 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_closest – If no intervals found, returns none.

Return type:

pandas.DataFrame

Notes

By default, direction is defined by the reference genome: everything with smaller coordinate is considered upstream, everything with larger coordinate is considered downstream.

If direction_col is provided, upstream/downstream are relative to the direction column in df1, i.e. features marked “+” and “.” strand will define upstream and downstream as above, while features marked “-” have upstream and downstream reversed: smaller coordinates are downstream and larger coordinates are upstream.

cluster(df, min_dist=0, cols=None, on=None, return_input=True, return_cluster_ids=True, return_cluster_intervals=True)

Cluster overlapping intervals into groups.

Can return numeric ids for these groups (with return_cluster_ids`=True) and/or their genomic coordinates (with `return_cluster_intervals`=True). Also see :func:`merge(), which discards original intervals and returns a new set.

Parameters:

• df (pandas.DataFrame) –

• min_dist (float or None) – If provided, cluster intervals separated by this distance or less. If None, do not cluster non-overlapping intervals. Since bioframe uses semi-open intervals, interval pairs [0,1) and [1,2) do not overlap, but are separated by a distance of 0. Such adjacent intervals are not clustered when min_dist=None, but are clustered when min_dist=0.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

• on (None or list) – List of column names to perform clustering on independently, passed as an argument to df.groupby before clustering. Default is None. An example useage would be to pass on=['strand'].

• return_input (bool) – If True, return input

• return_cluster_ids (bool) – If True, return ids for clusters

• return_cluster_invervals (bool) – If True, return clustered interval the original interval belongs to

Returns:

df_clustered

Return type:

pd.DataFrame

complement(df, view_df=None, view_name_col='name', cols=None, cols_view=None)

Find genomic regions in a viewFrame ‘view_df’ that are not covered by any interval in the dataFrame ‘df’.

First assigns intervals in ‘df’ to region in ‘view_df’, splitting intervals in ‘df’ as necessary.

Parameters:

• df (pandas.DataFrame) –

• view_df (pandas.Dataframe) – If none, attempts to infer the view from chroms (i.e. df[cols[0]]).

• view_name_col (str) – Name of column in view_df with unique reigon names. Default ‘name’.

• cols ((str, str, str)) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in the view. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_complement

Return type:

pandas.DataFrame

Notes

Discards null intervals in input, and df_complement has regular int dtype.

count_overlaps(df1, df2, suffixes=('', '_'), return_input=True, cols1=None, cols2=None, on=None)

Count number of overlapping genomic intervals.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• suffixes ((str, str)) – The suffixes for the columns of the two overlapped sets.

• return_input (bool) – If True, return columns from input dfs. Default True.

• cols1 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• on (list) – List of additional shared columns to consider as separate groups when considering overlaps. A common use would be passing on=[‘strand’]. Default is None.

Returns:

df_counts

Return type:

pandas.DataFrame

Notes

Resets index.

coverage(df1, df2, suffixes=('', '_'), return_input=True, cols1=None, cols2=None)

Quantify the coverage of intervals from ‘df1’ by intervals from ‘df2’.

For every interval in ‘df1’ find the number of base pairs covered by intervals in ‘df2’. Note this only quantifies whether a basepair in ‘df1’ was covered, as ‘df2’ is merged before calculating coverage.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• suffixes ((str, str)) – The suffixes for the columns of the two overlapped sets.

• return_input (bool) – If True, return input as well as computed coverage

• cols1 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_coverage

Return type:

pandas.DataFrame

Notes

Resets index.

expand(df, pad=None, scale=None, side='both', cols=None)

Expand each interval by an amount specified with pad.

Negative values for pad shrink the interval, up to the midpoint. Multiplicative rescaling of intervals enabled with scale. Only one of pad or scale can be provided. Often followed by trim().

Parameters:

• df (pandas.DataFrame) –

• pad (int, optional) – The amount by which the intervals are additively expanded on each side. Negative values for pad shrink intervals, but not beyond the interval midpoint. Either pad or scale must be supplied.

• scale (float, optional) – The factor by which to scale intervals multiplicatively on each side, e.g scale=2 doubles each interval, scale=0 returns midpoints, and scale=1 returns original intervals. Default False. Either pad or scale must be supplied.

• side (str, optional) – Which side to expand, possible values are ‘left’, ‘right’ and ‘both’. Default ‘both’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. Default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_expanded

Return type:

pandas.DataFrame

Notes

See bioframe.trim() for trimming interals after expansion.

merge(df, min_dist=0, cols=None, on=None)

Merge overlapping intervals.

This returns a new dataframe of genomic intervals, which have the genomic coordinates of the interval cluster groups from the input dataframe. Also cluster(), which returns the assignment of intervals to clusters prior to merging.

Parameters:

• df (pandas.DataFrame) –

• min_dist (float or None) – If provided, merge intervals separated by this distance or less. If None, do not merge non-overlapping intervals. Using min_dist=0 and min_dist=None will bring different results. bioframe uses semi-open intervals, so interval pairs [0,1) and [1,2) do not overlap, but are separated by a distance of 0. Adjacent intervals are not merged when min_dist=None, but are merged when min_dist=0.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

• on (None or list) – List of column names to perform clustering on independently, passed as an argument to df.groupby before clustering. Default is None. An example useage would be to pass on=['strand'].

Returns:

df_merged – A pandas dataframe with coordinates of merged clusters.

Return type:

pandas.DataFrame

Notes

Resets index.

overlap(df1, df2, how='left', return_input=True, return_index=False, return_overlap=False, suffixes=('', '_'), keep_order=None, cols1=None, cols2=None, on=None, ensure_int=True)

Find pairs of overlapping genomic intervals.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• how ({'left', 'right', 'outer', 'inner'}, default 'left') – How to handle the overlaps on the two dataframes. left: use the set of intervals in df1 right: use the set of intervals in df2 outer: use the union of the set of intervals from df1 and df2 inner: use intersection of the set of intervals from df1 and df2

• return_input (bool, optional) – If True, return columns from input dfs. Default True.

• return_index (bool, optional) – If True, return indicies of overlapping pairs as two new columns (‘index’+suffixes[0] and ‘index’+suffixes[1]). Default False.

• return_overlap (bool, optional) – If True, return overlapping intervals for the overlapping pairs as two additional columns (overlap_start, overlap_end). When cols1 is modified, start and end are replaced accordingly. When return_overlap is a string, its value is used for naming the overlap columns: return_overlap + “_start”, return_overlap + “_end”. Default False.

• suffixes ((str, str), optional) – The suffixes for the columns of the two overlapped sets.

• keep_order (bool, optional) – If True and how=’left’, sort the output dataframe to preserve the order of the intervals in df1. Cannot be used with how=’right’/’outer’/’inner’. Default True for how=’left’, and None otherwise. Note that it relies on sorting of index in the original dataframes, and will reorder the output by index.

• cols1 ((str, str, str) or None, optional) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None, optional) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• on (list or None, optional) – List of additional shared columns to consider as separate groups when considering overlaps. A common use would be passing on=[‘strand’]. Default is None.

• ensure_int (bool, optional [default: True]) – If True, ensures that the output dataframe uses integer dtypes for start and end coordinates. This may involve converting coordinate columns to nullable types in outer joins. Default True.

Returns:

df_overlap

Return type:

pandas.DataFrame

Notes

If ensure_int is False, inner joins will preserve coordinate dtypes from the input dataframes, but outer joins will be subject to native type casting rules if missing data is introduced. For example, if df1 uses a NumPy integer dtype for start and/or end, the output dataframe will use the same dtype after an inner join, but, due to casting rules, may produce float64 after a left/right/outer join with missing data stored as NaN. On the other hand, if df1 uses Pandas nullable dtypes, the corresponding coordinate columns will preserve the same dtype in the output, with missing data stored as NA.

select(df, region, cols=None)

Return all genomic intervals in a dataframe that overlap a genomic region.

Parameters:

• df (pandas.DataFrame) –

• region (str or tuple) – The genomic region to select from the dataframe in UCSC-style genomic region string, or triple (chrom, start, end).

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df

Return type:

pandas.DataFrame

Notes

See core.stringops.parse_region() for more information on region formatting.

See also

select_mask(), select_indices(), select_labels()

select_indices(df, region, cols=None)

Return integer indices of all genomic intervals that overlap a query range.

Parameters:

• df (pandas.DataFrame) –

• region (str or tuple) – The genomic region to select from the dataframe in UCSC-style genomic region string, or triple (chrom, start, end).

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

Return type:

1D array of int

select_labels(df, region, cols=None)

Return pandas Index labels of all genomic intervals that overlap a query range.

Parameters:

• df (pandas.DataFrame) –

• region (str or tuple) – The genomic region to select from the dataframe in UCSC-style genomic region string, or triple (chrom, start, end).

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

Return type:

pandas.Index

select_mask(df, region, cols=None)

Return boolean mask for all genomic intervals that overlap a query range.

Parameters:

• df (pandas.DataFrame) –

• region (str or tuple) – The genomic region to select from the dataframe in UCSC-style genomic region string, or triple (chrom, start, end).

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

Return type:

Boolean array of shape (len(df),)

setdiff(df1, df2, cols1=None, cols2=None, on=None)

Generate a new dataframe of genomic intervals by removing any interval from the first dataframe that overlaps an interval from the second dataframe.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as DataFrames.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as DataFrames.

• cols1 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each dataframe. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each dataframe. The default values are ‘chrom’, ‘start’, ‘end’.

• on (None or list) – Additional column names to perform clustering on independently, passed as an argument to df.groupby when considering overlaps and must be present in both dataframes. Examples for additional columns include ‘strand’.

Returns:

df_setdiff

Return type:

pandas.DataFrame

sort_bedframe(df, view_df=None, reset_index=True, df_view_col=None, view_name_col='name', cols=None, cols_view=None)

Sorts a bedframe ‘df’.

If ‘view_df’ is not provided, sorts by cols (e.g. “chrom”, “start”, “end”). If ‘view_df’ is provided and ‘df_view_col’ is not provided, uses bioframe.ops.assign_view() with df_view_col='view_region' to assign intervals to the view regions with the largest overlap and then sorts. If ‘view_df’ and ‘df_view_col’ are both provided, checks if the latter are cataloged in ‘view_name_col’, and then sorts.

dfpandas.DataFrame

Valid bedframe.

view_dfpandas.DataFrame | dict-like

Valid input to make a viewframe. When it is dict-like :func:’bioframe.make_viewframe’ will be used to convert to viewframe. If view_df is not provided df is sorted by chrom and start.

reset_indexbool

Default True.

df_view_col: None | str

Column from ‘df’ used to associate intervals with view regions. The associated region in ‘view_df’ is then used for sorting. If None, :func:’bioframe.assign_view’ will be used to assign view regions. Default None.

view_name_col: str

Column from view_df with names of regions. Default name.

cols(str, str, str) or None

The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

cols_view(str, str, str) or None

The names of columns containing the chromosome, start and end of the genomic intervals in the view. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

out_df

Return type:

sorted bedframe

Notes

df_view_col is currently returned as an ordered categorical

subtract(df1, df2, return_index=False, suffixes=('', '_'), cols1=None, cols2=None)

Generate a new set of genomic intervals by subtracting the second set of genomic intervals from the first.

Parameters:

• df1 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• df2 (pandas.DataFrame) – Two sets of genomic intervals stored as a DataFrame.

• return_index (bool) – Whether to return the indices of the original intervals (‘index’+suffixes[0]), and the indices of any sub-intervals split by subtraction (‘sub_index’+suffixes[1]). Default False.

• suffixes ((str,str)) – Suffixes for returned indices. Only alters output if return_index is True. Default (“”,”_”).

• cols1 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• cols2 ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_subtracted

Return type:

pandas.DataFrame

Notes

Resets index, drops completely subtracted (null) intervals, and casts to pd.Int64Dtype().

trim(df, view_df=None, df_view_col=None, view_name_col='name', return_view_columns=False, cols=None, cols_view=None)

Trim each interval to fall within regions specified in the viewframe ‘view_df’.

Intervals that fall outside of view regions are replaced with nulls. If no ‘view_df’ is provided, intervals are truncated at zero to avoid

negative values.

Parameters:

• df (pandas.DataFrame) –

• view_df (None or pandas.DataFrame) – View specifying region start and ends for trimming. Attempts to convert dictionary and pd.Series formats to viewFrames.

• df_view_col (str or None) – The column of ‘df’ used to specify view regions. The associated region in ‘view_df’ is then used for trimming. If None, :func:’bioframe.ops.assign_view’ will be used to assign view regions. If no ‘view_df’ is provided, uses the ‘chrom’ column, df[cols[0]]. Default None.

• view_name_col (str) – Column of df with region names. Default ‘name’.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals. The default values are ‘chrom’, ‘start’, ‘end’.

• cols_view ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals in the view. The default values are ‘chrom’, ‘start’, ‘end’.

Returns:

df_trimmed

Return type:

pandas.DataFrame

File I/O

Functions:

load_fasta(filepath_or[, engine])	Load lazy fasta sequences from an indexed fasta file (optionally compressed) or from a collection of uncompressed fasta files.
read_bam(fp[, chrom, start, end])	Read bam records into a DataFrame.
read_bigbed(path, chrom[, start, end, engine])	Read intervals from a bigBed file.
read_bigwig(path, chrom[, start, end, engine])	Read intervals from a bigWig file.
read_chromsizes(filepath_or[, ...])	Read a <db>.chrom.sizes or <db>.chromInfo.txt file from the UCSC database, where db is a genome assembly name, as a pandas.Series.
read_pairix(fp, region1[, region2, ...])	Read a pairix-indexed file into DataFrame.
read_tabix(fp[, chrom, start, end])	Read a tabix-indexed file into dataFrame.
read_table(filepath_or[, schema, ...])	Read a tab-delimited file into a data frame.
to_bigbed(df, chromsizes, outpath[, schema, ...])	Save a bedGraph-like dataframe as a binary BigWig track.
to_bigwig(df, chromsizes, outpath[, ...])	Save a bedGraph-like dataframe as a binary BigWig track.

load_fasta(filepath_or, engine='pysam', **kwargs)

Load lazy fasta sequences from an indexed fasta file (optionally compressed) or from a collection of uncompressed fasta files.

Parameters:

• filepath_or (str or iterable) – If a string, a filepath to a single .fa or .fa.gz file. Assumed to be accompanied by a .fai index file. Depending on the engine, the index may be created on the fly, and some compression formats may not be supported. If not a string, an iterable of fasta file paths each assumed to contain a single sequence.

• engine ({'pysam', 'pyfaidx'}, optional) – Module to use for loading sequences.

• kwargs (optional) – Options to pass to pysam.FastaFile or pyfaidx.Fasta.

Return type:

OrderedDict of (lazy) fasta records.

Notes

• pysam/samtools can read .fai and .gzi indexed files, I think.

• pyfaidx can handle uncompressed and bgzf compressed files.

read_bam(fp, chrom=None, start=None, end=None)

Read bam records into a DataFrame.

read_bigbed(path, chrom, start=None, end=None, engine='auto')

Read intervals from a bigBed file.

Parameters:

• path (str) – Path or URL to a bigBed file

• chrom (str) –

• start (int, optional) – Start and end coordinates. Defaults to 0 and chromosome length.

• end (int, optional) – Start and end coordinates. Defaults to 0 and chromosome length.

• engine ({"auto", "pybbi", "pybigwig"}) – Library to use for querying the bigBed file.

Return type:

DataFrame

read_bigwig(path, chrom, start=None, end=None, engine='auto')

Read intervals from a bigWig file.

Parameters:

• path (str) – Path or URL to a bigWig file

• chrom (str) –

• start (int, optional) – Start and end coordinates. Defaults to 0 and chromosome length.

• end (int, optional) – Start and end coordinates. Defaults to 0 and chromosome length.

• engine ({"auto", "pybbi", "pybigwig"}) – Library to use for querying the bigWig file.

Return type:

DataFrame

read_chromsizes(filepath_or, filter_chroms=True, chrom_patterns=('^chr[0-9]+$', '^chr[XY]$', '^chrM$'), natsort=True, as_bed=False, **kwargs)

Read a <db>.chrom.sizes or <db>.chromInfo.txt file from the UCSC database, where db is a genome assembly name, as a pandas.Series.

Parameters:

• filepath_or (str or file-like) – Path or url to text file, or buffer.

• filter_chroms (bool, optional) – Filter for chromosome names given in chrom_patterns.

• chrom_patterns (sequence, optional) – Sequence of regular expressions to capture desired sequence names.

• natsort (bool, optional) – Sort each captured group of names in natural order. Default is True.

• as_bed (bool, optional) – If True, return chromsizes as an interval dataframe (chrom, start, end).

• **kwargs – Passed to pandas.read_csv()

Return type:

Series of integer bp lengths indexed by sequence name or an interval dataframe.

Notes

Mention name patterns

See also

• UCSC assembly terminology: <http://genome.ucsc.edu/FAQ/FAQdownloads.html#download9>

• NCBI assembly terminology: <https://www.ncbi.nlm.nih.gov/grc/help/definitions>

read_pairix(fp, region1, region2=None, chromsizes=None, columns=None, usecols=None, dtypes=None, **kwargs)

Read a pairix-indexed file into DataFrame.

read_tabix(fp, chrom=None, start=None, end=None)

Read a tabix-indexed file into dataFrame.

read_table(filepath_or, schema=None, schema_is_strict=False, **kwargs)

Read a tab-delimited file into a data frame.

Equivalent to pandas.read_table() but supports an additional schema argument to populate column names for common genomic formats.

Parameters:

• filepath_or (str, path object or file-like object) – Any valid string path is acceptable. The string could be a URL

• schema (str) – Schema to use for table column names.

• schema_is_strict (bool) – Whether to check if columns are filled with NAs.

Returns:

df

Return type:

pandas.DataFrame of intervals

to_bigbed(df, chromsizes, outpath, schema='bed6', path_to_binary=None)

Save a bedGraph-like dataframe as a binary BigWig track.

Parameters:

• df (pandas.DataFrame) – Data frame with columns ‘chrom’, ‘start’, ‘end’ and one or more value columns

• chromsizes (pandas.Series) – Series indexed by chromosome name mapping to their lengths in bp

• outpath (str) – The output BigWig file path

• value_field (str, optional) – Select the column label of the data frame to generate the track. Default is to use the fourth column.

• path_to_binary (str, optional) – Provide system path to the bedGraphToBigWig binary.

to_bigwig(df, chromsizes, outpath, value_field=None, path_to_binary=None)

Save a bedGraph-like dataframe as a binary BigWig track.

Parameters:

• df (pandas.DataFrame) – Data frame with columns ‘chrom’, ‘start’, ‘end’ and one or more value columns

• chromsizes (pandas.Series) – Series indexed by chromosome name mapping to their lengths in bp

• outpath (str) – The output BigWig file path

• value_field (str, optional) – Select the column label of the data frame to generate the track. Default is to use the fourth column.

• path_to_binary (str, optional) – Provide system path to the bedGraphToBigWig binary.

Resources

Genome assembly metadata

Bioframe provides a collection of genome assembly metadata for commonly used genomes. These are accessible through a convenient dataclass interface via bioframe.assembly_info().

The assemblies are listed in a manifest YAML file, and each assembly has a mandatory companion file called seqinfo that contains the sequence names, lengths, and other information. The records in the manifest file contain the following fields:

• organism: the organism name

• provider: the genome assembly provider (e.g, ucsc, ncbi)

• provider_build: the genome assembly build name (e.g., hg19, GRCh37)

• release_year: the year of the assembly release

• seqinfo: path to the seqinfo file

• cytobands: path to the cytoband file, if available

• default_roles: default molecular roles to include from the seqinfo file

• default_units: default assembly units to include from the seqinfo file

• url: URL to where the corresponding sequence files can be downloaded

The seqinfo file is a TSV file with the following columns (with header):

• name: canonical sequence name

• length: sequence length

• role: role of the sequence or scaffold (e.g., “assembled”, “unlocalized”, “unplaced”)

• molecule: name of the molecule that the sequence belongs to, if placed

• unit: assembly unit of the chromosome (e.g., “primary”, “non-nuclear”, “decoy”)

• aliases: comma-separated list of aliases for the sequence name

We currently do not include sequences with “alt” or “patch” roles in seqinfo files, but we do support the inclusion of additional decoy sequences (as used by so-called NGS analysis sets for human genome assemblies) by marking them as members of a “decoy” assembly unit.

The cytoband file is an optional TSV file with the following columns (with header):

• chrom: chromosome name

• start: start position

• end: end position

• band: cytogenetic coordinate (name of the band)

• stain: Giesma stain result

The order of the sequences in the seqinfo file is treated as canonical. The ordering of the chromosomes in the cytobands file should match the order of the chromosomes in the seqinfo file.

The manifest and companion files are stored in the bioframe/io/data directory. New assemblies can be requested by opening an issue on GitHub or by submitting a pull request.

Functions:

assemblies_available()	Get a list of available genome assembly metadata in local storage.
assembly_info(name[, roles, units])	Get information about a genome assembly.

assemblies_available() → DataFrame

Get a list of available genome assembly metadata in local storage.

Returns:

A dataframe with metadata fields for available assemblies, including ‘provider’, ‘provider_build’, ‘default_roles’, ‘default_units’, and names of seqinfo and cytoband files.

Return type:

pandas.DataFrame

assembly_info(name: str, roles: List | Tuple | Literal['all'] | None = None, units: List | Tuple | Literal['all'] | None = None) → GenomeAssembly

Get information about a genome assembly.

Parameters:

• name (str) – Name of the assembly. If the name contains a dot, it is interpreted as a provider name and a build, e.g. “hg38”. Otherwise, the provider is inferred if the build name is unique.

• roles (list or tuple or "all", optional) – Sequence roles to include in the assembly info. If not specified, only sequences with the default sequence roles for the assembly are shown. e.g. “assembled”, “unlocalized”, “unplaced”

• units (list or tuple or "all", optional) – Assembly units to include in the assembly info. If not specified, only sequences from the default units for the assembly are shown. e.g. “primary”, “non-nuclear”, “decoy”

Returns:

A dataclass containing information about the assembly.

Return type:

GenomeAssembly

Raises:

ValueError – If the assembly name is not found or is not unique.

Examples

>>> hg38 = assembly_info("hg38")
>>> hg38.chromsizes
name
chr1    248956422
chr2    242193529
chr3    198295559
...     ...

>>> assembly_info("hg38", roles=("assembled", "non-nuclear"))

>>> assembly_info("ucsc.hg38", units=("unplaced",))

class GenomeAssembly(organism: str, provider: str, provider_build: str, release_year: str, seqinfo: DataFrame, cytobands: DataFrame | None = None, url: str | None = None, alias_dict: Dict[str, str] | None = None)

A dataclass containing information about sequences in a genome assembly.

alias_dict: Dict[str, str] = None

property chromnames: List[str]

property chromsizes: Series

cytobands: DataFrame = None

organism: str

provider: str

provider_build: str

release_year: str

seqinfo: DataFrame

url: str = None

property viewframe: DataFrame

Remote resources

These functions now default to using the local data store, but can be used to obtain chromsizes or centromere positions from UCSC by setting provider="ucsc".

Functions:

fetch_centromeres(db[, provider])	Extract centromere locations for a given assembly 'db' from a variety of file formats in UCSC (cytoband, centromeres) depending on availability, returning a DataFrame.
fetch_chromsizes(db, *[, provider, as_bed, ...])	Fetch chromsizes from local storage or the UCSC database.

fetch_centromeres(db: str, provider: str = 'local') → DataFrame

Extract centromere locations for a given assembly ‘db’ from a variety of file formats in UCSC (cytoband, centromeres) depending on availability, returning a DataFrame.

Parameters:

• db (str) – Assembly name.

• provider (str, optional [default: "local"]) – The provider of centromere data. Either “local” for local storage or “ucsc”.

Return type:

DataFrame with centromere ‘chrom’, ‘start’, ‘end’, ‘mid’.

Notes

When provider=”local”, centromeres are derived from cytoband tables in local storage.

Whe provider=”ucsc”, the fallback priority goes as follows: - UCSC cytoBand - UCSC cytoBandIdeo - UCSC centromeres.txt

Note that UCSC “gap” files no longer provide centromere information.

Currently only works for human assemblies.

See also

bioframe.assembly_info, bioframe.UCSCClient

fetch_chromsizes(db: str, *, provider: str = 'local', as_bed: bool = False, filter_chroms: bool = True, chrom_patterns: tuple = ('^chr[0-9]+$', '^chr[XY]$', '^chrM$'), natsort: bool = True, **kwargs) → Series | DataFrame

Fetch chromsizes from local storage or the UCSC database.

Parameters:

• db (str) – Assembly name.

• provider (str, optional [default: "local"]) – The provider of chromsizes. Either “local” for local storage or “ucsc”.

• as_bed (bool, optional) – If True, return chromsizes as an interval DataFrame (chrom, start, end) instead of a Series.

• provider="ucsc". (The remaining options only apply to) –

• filter_chroms (bool, optional) – Filter for chromosome names given in chrom_patterns.

• chrom_patterns (sequence, optional) – Sequence of regular expressions to capture desired sequence names.

• natsort (bool, optional) – Sort each captured group of names in natural order. Default is True.

• **kwargs – Passed to pandas.read_csv()

Return type:

Series of integer bp lengths indexed by sequence name or BED3 DataFrame.

Notes

For more fine-grained control over the chromsizes from local storage, use bioframe.assembly_info().

Examples

>>> fetch_chromsizes("hg38")
name
chr1     248956422
chr2     242193529
chr3     198295559
...      ...
chrX     156040895
chrY      57227415
chrM         16569
Name: length, dtype: int64

>>> fetch_chromsizes("hg38", as_bed=True)
        chrom      start        end
0        chr1          0  248956422
1        chr2          0  242193529
2        chr3          0  198295559
...      ...
21       chrX          0  156040895
22       chrY          0   57227415
23       chrM          0      16569

See also

bioframe.assembly_info, bioframe.UCSCClient

Additional tools

Functions:

binnify(chromsizes, binsize[, rel_ids])	Divide a genome into evenly sized bins.
digest(fasta_records, enzyme)	Divide a genome into restriction fragments.
frac_gc(df, fasta_records[, mapped_only, ...])	Calculate the fraction of GC basepairs for each interval in a dataframe.
frac_gene_coverage(df, ucsc_mrna)	Calculate number and fraction of overlaps by predicted and verified RNA isoforms for a set of intervals stored in a dataframe.
frac_mapped(df, fasta_records[, return_input])	Calculate the fraction of mapped base-pairs for each interval in a dataframe.
make_chromarms(chromsizes, midpoints[, ...])	Split chromosomes into chromosome arms.
pair_by_distance(df, min_sep, max_sep[, ...])	From a dataframe of genomic intervals, find all unique pairs of intervals that are between min_sep and max_sep bp separated from each other.
seq_gc(seq[, mapped_only])	Calculate the fraction of GC basepairs for a string of nucleotides.

binnify(chromsizes, binsize, rel_ids=False)

Divide a genome into evenly sized bins.

Parameters:

• chromsizes (Series) – pandas Series indexed by chromosome name with chromosome lengths in bp.

• binsize (int) – size of bins in bp

Returns:

bintable

Return type:

pandas.DataFrame with columns: ‘chrom’, ‘start’, ‘end’.

digest(fasta_records, enzyme)

Divide a genome into restriction fragments.

Parameters:

• fasta_records (OrderedDict) – Dictionary of chromosome names to sequence records. Created by: bioframe.load_fasta(‘/path/to/fasta.fa’)

• enzyme (str) – Name of restriction enzyme.

Returns:

Dataframe with columns

Return type:

‘chrom’, ‘start’, ‘end’.

frac_gc(df, fasta_records, mapped_only=True, return_input=True)

Calculate the fraction of GC basepairs for each interval in a dataframe.

Parameters:

• df (pandas.DataFrame) – A sets of genomic intervals stored as a DataFrame.

• fasta_records (OrderedDict) – Dictionary of chromosome names to sequence records. Created by: bioframe.load_fasta(‘/path/to/fasta.fa’)

• mapped_only (bool) – if True, ignore ‘N’ in the fasta_records for calculation. if True and there are no mapped base-pairs in an interval, return np.nan.

• return_input (bool) – if False, only return Series named frac_mapped.

Returns:

df_mapped – Original dataframe with new column ‘GC’ appended.

Return type:

pd.DataFrame

frac_gene_coverage(df, ucsc_mrna)

Calculate number and fraction of overlaps by predicted and verified RNA isoforms for a set of intervals stored in a dataframe.

Parameters:

• df (pd.DataFrame) – Set of genomic intervals stored as a dataframe.

• ucsc_mrna (str or DataFrame) – Name of UCSC genome or all_mrna.txt dataframe from UCSC or similar.

Returns:

df_gene_coverage

Return type:

pd.DataFrame

frac_mapped(df, fasta_records, return_input=True)

Calculate the fraction of mapped base-pairs for each interval in a dataframe.

Parameters:

• df (pandas.DataFrame) – A sets of genomic intervals stored as a DataFrame.

• fasta_records (OrderedDict) – Dictionary of chromosome names to sequence records. Created by: bioframe.load_fasta(‘/path/to/fasta.fa’)

• return_input (bool) – if False, only return Series named frac_mapped.

Returns:

df_mapped – Original dataframe with new column ‘frac_mapped’ appended.

Return type:

pd.DataFrame

make_chromarms(chromsizes, midpoints, cols_chroms=('chrom', 'length'), cols_mids=('chrom', 'mid'), suffixes=('_p', '_q'))

Split chromosomes into chromosome arms.

Parameters:

• chromsizes (pandas.Dataframe or dict-like) – If dict or pandas.Series, a map from chromosomes to lengths in bp. If pandas.Dataframe, a dataframe with columns defined by cols_chroms. If cols_chroms is a triplet (e.g. ‘chrom’,’start’,’end’), then values in chromsizes[cols_chroms[1]].values must all be zero.

• midpoints (pandas.Dataframe or dict-like) – Mapping of chromosomes to midpoint (aka centromere) locations. If dict or pandas.Series, a map from chromosomes to midpoints in bp. If pandas.Dataframe, a dataframe with columns defined by cols_mids.

• cols_chroms ((str, str) or (str, str, str)) – Two columns

• suffixes (tuple, optional) – Suffixes to name chromosome arms. Defaults to p and q.

Returns:

4-column BED-like DataFrame (chrom, start, end, name). Arm names are chromosome names + suffix. Any chromosome not included in mids will be not be split.

Return type:

df_chromarms

pair_by_distance(df, min_sep, max_sep, min_intervening=None, max_intervening=None, relative_to='midpoints', cols=None, return_index=False, keep_order=False, suffixes=('_1', '_2'))

From a dataframe of genomic intervals, find all unique pairs of intervals that are between min_sep and max_sep bp separated from each other.

Parameters:

• df (pandas.DataFrame) – A BED-like dataframe.

• min_sep (int) – Minimum and maximum separation between intervals in bp. Min > 0 and Max >= Min.

• max_sep (int) – Minimum and maximum separation between intervals in bp. Min > 0 and Max >= Min.

• min_intervening (int) – Minimum and maximum number of intervening intervals separating pairs. Min > 0 and Max >= Min.

• max_intervening (int) – Minimum and maximum number of intervening intervals separating pairs. Min > 0 and Max >= Min.

• relative_to (str,) – Whether to calculate distances between interval “midpoints” or “endpoints”. Default “midpoints”.

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• return_index (bool) – If True, return indicies of pairs as two new columns (‘index’+suffixes[0] and ‘index’+suffixes[1]). Default False.

• keep_order (bool, optional) – If True, sort the output dataframe to preserve the order of the intervals in df1. Default False. Note that it relies on sorting of index in the original dataframes, and will reorder the output by index.

• suffixes ((str, str), optional) – The column name suffixes for the two interval sets in the output. The first interval of each output pair is always upstream of the second.

Returns:

A BEDPE-like dataframe of paired intervals from df.

Return type:

pandas.DataFrame

seq_gc(seq, mapped_only=True)

Calculate the fraction of GC basepairs for a string of nucleotides.

Parameters:

• seq (str) – Basepair input

• mapped_only (bool) – if True, ignore ‘N’ in the sequence for calculation. if True and there are no mapped base-pairs, return np.nan.

Returns:

gc – calculated gc content.

Return type:

float

Plotting

Functions:

plot_intervals(df[, levels, labels, colors, ...])	Plot a collection of intervals, one plot per chromosome.
to_ucsc_colorstring(color)	Convert any matplotlib color identifier into a UCSC itemRgb color string.

plot_intervals(df, levels=None, labels=None, colors=None, xlim=None, show_coords=False, figsize=(10, 2))

Plot a collection of intervals, one plot per chromosome.

Parameters:

• df (pandas.DataFrame) – A collection of intervals.

• levels (iterable or None) – The level of each interval, i.e. the y-coordinate at which the interval must be plotted. If None, it will be determined automatically.

• labels (str or iterable or None) – The label of each interval.

• colors (str or iterable or None.) – The color of each interval.

• xlim ((float, float) or None) – The x-span of the plot.

• show_coords (bool) – If True, plot x-ticks.

• figsize ((float, float) or None.) – The size of the figure. If None, plot within the current figure.

to_ucsc_colorstring(color: str | tuple) → str

Convert any matplotlib color identifier into a UCSC itemRgb color string.

Parameters:

color (str or tuple) – Any valid matplotlib color representation (e.g. ‘red’, ‘tomato’, ‘#ff0000’, ‘#ff00’, “#ff000055”, (1, 0, 0), (1, 0, 0, 0.5))

Returns:

A UCSC itemRgb colorstring of the form “r,g,b” where r, g, and b are integers between 0 and 255, inclusive.

Return type:

str

Notes

The alpha (opacity) channel is ignored if represented in the input.

Null values are converted to “0”, which is shorthand for “0,0,0” (black). Note that BED9+ files with uninformative itemRgb values should use “0” as the itemRgb value on every data line.

Examples

>>> to_ucsc_colorstring("red")
'255,0,0'
>>> to_ucsc_colorstring("tomato")
'255,99,71'
>>> df["itemRgb"] = df["color"].apply(to_ucsc_colorstring)
>>> df
chrom  start  end  color  itemRgb
chr1   0      10   red    255,0,0
chr1   10     20   blue   0,0,255
chr2   0      10   green  0,128,0
chr2   10     20   None   0

Low-level API

Array operations

Low level operations that are used to implement the genomic interval operations.

Functions:

arange_multi(starts[, stops, lengths])	Create concatenated ranges of integers for multiple start/length.
closest_intervals(starts1, ends1[, starts2, ...])	For every interval in set 1, return the indices of k closest intervals from set 2.
interweave(a, b)	Interweave two arrays.
merge_intervals(starts, ends[, min_dist])	Merge overlapping intervals.
overlap_intervals(starts1, ends1, starts2, ends2)	Take two sets of intervals and return the indices of pairs of overlapping intervals.
overlap_intervals_outer(starts1, ends1, ...)	Take two sets of intervals and return the indices of pairs of overlapping intervals, as well as the indices of the intervals that do not overlap any other interval.
sum_slices(arr, starts, ends)	Calculate sums of slices of an array.

arange_multi(starts, stops=None, lengths=None)

Create concatenated ranges of integers for multiple start/length.

Parameters:

• starts (numpy.ndarray) – Starts for each range

• stops (numpy.ndarray) – Stops for each range

• lengths (numpy.ndarray) – Lengths for each range. Either stops or lengths must be provided.

Returns:

concat_ranges – Concatenated ranges.

Return type:

numpy.ndarray

Notes

See the following illustrative example:

starts = np.array([1, 3, 4, 6]) stops = np.array([1, 5, 7, 6])

print arange_multi(starts, lengths) >>> [3 4 4 5 6]

From: https://codereview.stackexchange.com/questions/83018/vectorized-numpy-version-of-arange-with-multiple-start-stop

closest_intervals(starts1, ends1, starts2=None, ends2=None, k=1, tie_arr=None, ignore_overlaps=False, ignore_upstream=False, ignore_downstream=False, direction=None)

For every interval in set 1, return the indices of k closest intervals from set 2.

Parameters:

• starts1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored. If start2 and ends2 are None, find closest intervals within the same set.

• ends1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored. If start2 and ends2 are None, find closest intervals within the same set.

• starts2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored. If start2 and ends2 are None, find closest intervals within the same set.

• ends2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored. If start2 and ends2 are None, find closest intervals within the same set.

• k (int) – The number of neighbors to report.

• tie_arr (numpy.ndarray or None) – Extra data describing intervals in set 2 to break ties when multiple intervals are located at the same distance. Intervals with lower tie_arr values will be given priority.

• ignore_overlaps (bool) – If True, ignore set 2 intervals that overlap with set 1 intervals.

• ignore_upstream (bool) – If True, ignore set 2 intervals upstream/downstream of set 1 intervals.

• ignore_downstream (bool) – If True, ignore set 2 intervals upstream/downstream of set 1 intervals.

• direction (numpy.ndarray with dtype bool or None) – Strand vector to define the upstream/downstream orientation of the intervals.

Returns:

closest_ids – An Nx2 array containing the indices of pairs of closest intervals. The 1st column contains ids from the 1st set, the 2nd column has ids from the 2nd set.

Return type:

numpy.ndarray

interweave(a, b)

Interweave two arrays.

Parameters:

• a (numpy.ndarray) – Arrays to interweave, must have the same length/

• b (numpy.ndarray) – Arrays to interweave, must have the same length/

Returns:

out – Array of interweaved values from a and b.

Return type:

numpy.ndarray

Notes

From https://stackoverflow.com/questions/5347065/interweaving-two-numpy-arrays

merge_intervals(starts, ends, min_dist=0)

Merge overlapping intervals.

Parameters:

• starts (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• ends (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• min_dist (float or None) – If provided, merge intervals separated by this distance or less. If None, do not merge non-overlapping intervals. Using min_dist=0 and min_dist=None will bring different results. bioframe uses semi-open intervals, so interval pairs [0,1) and [1,2) do not overlap, but are separated by a distance of 0. Such intervals are not merged when min_dist=None, but are merged when min_dist=0.

Returns:

• cluster_ids (numpy.ndarray) – The indices of interval clusters that each interval belongs to.

• cluster_starts (numpy.ndarray)

• cluster_ends (numpy.ndarray) – The spans of the merged intervals.

Notes

From https://stackoverflow.com/questions/43600878/merging-overlapping-intervals/58976449#58976449

overlap_intervals(starts1, ends1, starts2, ends2, closed=False, sort=False)

Take two sets of intervals and return the indices of pairs of overlapping intervals.

Parameters:

• starts1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• ends1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• starts2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• ends2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• closed (bool) – If True, then treat intervals as closed and report single-point overlaps.

Returns:

overlap_ids – An Nx2 array containing the indices of pairs of overlapping intervals. The 1st column contains ids from the 1st set, the 2nd column has ids from the 2nd set.

Return type:

numpy.ndarray

overlap_intervals_outer(starts1, ends1, starts2, ends2, closed=False)

Take two sets of intervals and return the indices of pairs of overlapping intervals, as well as the indices of the intervals that do not overlap any other interval.

Parameters:

• starts1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• ends1 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• starts2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• ends2 (numpy.ndarray) – Interval coordinates. Warning: if provided as pandas.Series, indices will be ignored.

• closed (bool) – If True, then treat intervals as closed and report single-point overlaps.

Returns:

• overlap_ids (numpy.ndarray) – An Nx2 array containing the indices of pairs of overlapping intervals. The 1st column contains ids from the 1st set, the 2nd column has ids from the 2nd set.

• no_overlap_ids1, no_overlap_ids2 (numpy.ndarray) – Two 1D arrays containing the indices of intervals in sets 1 and 2 respectively that do not overlap with any interval in the other set.

sum_slices(arr, starts, ends)

Calculate sums of slices of an array.

Parameters:

• arr (numpy.ndarray) –

• starts (numpy.ndarray) – Starts for each slice

• ends (numpy.ndarray) – Stops for each slice

Returns:

sums – Sums of the slices.

Return type:

numpy.ndarray

Specifications

Functions:

is_chrom_dtype(chrom_dtype)

Returns True if dtype is any of the allowed bioframe chrom dtypes, False otherwise.

is_chrom_dtype(chrom_dtype)

Returns True if dtype is any of the allowed bioframe chrom dtypes, False otherwise.

Unexposed functions:

specs._verify_column_dtypes(cols=None, return_as_bool=False)

Checks that dataframe df has chrom, start, end columns with valid dtypes. Raises TypeErrors if cols have invalid dtypes.

Parameters:

• df (pandas.DataFrame) –

• cols ((str, str, str) or None) – The names of columns containing the chromosome, start and end of the genomic intervals, provided separately for each set. The default values are ‘chrom’, ‘start’, ‘end’.

• return_as_bool (bool) – If true, returns as a boolean instead of raising errors. Default False.

specs._verify_columns(colnames, unique_cols=False, return_as_bool=False)

Raises ValueError if columns with colnames are not present in dataframe df.

Parameters:

• df (pandas.DataFrame) –

• colnames (list of column names) –

• return_as_bool (bool) – If True, returns as a boolean instead of raising errors. Default False.

specs._get_default_colnames()

Returns default column names.

These defaults be updated with update_default_colnames().

Returns:

colnames

Return type:

triplet (str, str, str)

String operations

Functions:

is_complete_ucsc_string(s)	Returns True if a string can be parsed into a completely informative (chrom, start, end) format.
parse_region(grange[, chromsizes, check_bounds])	Coerce a genomic range string or sequence type into a triple.
parse_region_string(s)	Parse a UCSC-style genomic range string into a triple.
to_ucsc_string(grange)	Convert a grange to a UCSC string.

is_complete_ucsc_string(s: str) → bool

Returns True if a string can be parsed into a completely informative (chrom, start, end) format.

Parameters:

s (str) –

Returns:

True if able to be parsed and end is known.

Return type:

bool

parse_region(grange: str | tuple, chromsizes: dict | Series | None = None, *, check_bounds: bool = True) → Tuple[str, int, int]

Coerce a genomic range string or sequence type into a triple.

Parameters:

• grange (str or tuple) –

  • A UCSC-style genomic range string, e.g. “chr5:10,100,000-30,000,000”.

  • A triple (chrom, start, end), where start or end may be None.

  • A quadruple or higher-order tuple, e.g. (chrom, start, end, name). name and other fields will be ignored.

• chromsizes (dict or Series, optional) – Lookup table of sequence lengths for bounds checking and for filling in a missing end coordinate.

• check_bounds (bool, optional [default: True]) – If True, check that the genomic range is within the bounds of the sequence.

Returns:

A well-formed genomic range triple (str, int, int).

Return type:

tuple

Notes

Genomic ranges are interpreted as half-open intervals (0-based starts, 1-based ends) along the length coordinate of a sequence.

Sequence names may contain any character except for whitespace and colon.

The start coordinate should be 0 or greater and the end coordinate should be less than or equal to the length of the sequence, if the latter is known. These are enforced when check_bounds is True.

If the start coordinate is missing, it is assumed to be 0. If the end coordinate is missing and chromsizes are provided, it is replaced with the length of the sequence.

The end coordinate must be greater than or equal to the start.

The start and end coordinates may be suffixed with k(b), M(b), or G(b) multipliers, case-insentive. e.g. “chr1:1K-2M” is equivalent to “chr1:1000-2000000”.

parse_region_string(s: str) → Tuple[str, int, int]

Parse a UCSC-style genomic range string into a triple.

Parameters:

s (str) – UCSC-style genomic range string, e.g. “chr5:10,100,000-30,000,000”.

Returns:

(str, int or None, int or None)

Return type:

tuple

See also

parse_region

to_ucsc_string(grange: Tuple[str, int, int]) → str

Convert a grange to a UCSC string.

Parameters:

grange (tuple or other iterable) – chrom, start, end

Returns:

UCSC-style genomic range string, ‘{chrom}:{start}-{end}’

Return type:

str

Low level array-based operations are used to implement the genomic interval operations on dataframes.

import itertools

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd

import bioframe as bf
import bioframe.vis


from bioframe.core import arrops

starts1, ends1 = np.array([
    [1,5],
    [3,8],
    [8,10],
    [12,14]
]).T

starts2, ends2 = np.array([
    [4,8],
    [10,11],
]).T

bf.vis.plot_intervals_arr(
    starts = starts1,
    ends = ends1,
    xlim = (-0.5,14.5),
    labels = np.arange(0,starts1.shape[0]),
    show_coords = True)

bf.vis.plot_intervals_arr(
    starts = starts2,
    ends = ends2,
    colors = 'lightpink',
    xlim = (-0.5,14.5),
    labels = np.arange(0,starts2.shape[0]),
    show_coords = True)

arrops.overlap_intervals(starts1, ends1, starts2, ends2)

array([[0, 0],
       [1, 0]])

arrops.overlap_intervals_outer(starts1, ends1, starts2, ends2)

(array([[0, 0],
        [1, 0]]),
 array([2, 3]),
 array([1]))

arrops.merge_intervals(starts1, ends1, min_dist=0)

(array([0, 0, 0, 1]), array([ 1, 12]), array([10, 14]))

arrops.merge_intervals(starts1, ends1, min_dist=None)

(array([0, 0, 1, 2]), array([ 1,  8, 12]), array([ 8, 10, 14]))

arrops.merge_intervals(starts1, ends1, min_dist=2)

(array([0, 0, 0, 0]), array([1]), array([14]))

arrops.complement_intervals(starts1, ends1)

(array([ 0, 10, 14]),
 array([                  1,                  12, 9223372036854775807]))

Indices and tables

• Index

• Module Index

• Search Page