pyBedGraph 0.5.38

A package for fast operations on 1-dimensional genomic signal tracks

pyBedGraph

A Python package for fast operations on 1-dimensional genomic signal tracks.

Features

• Finds the mean, approx. mean, max, min, coverage, or standard deviation for a given interval in a bedGraph file
• Partly written in Cython for speed improvements
• Can look up exact statistics of 1 million regions in ~0.26 second on a conventional laptop
• An approximate mean for 10,000 regions can be computed in ~0.0012 second w/ error rate of less than 5 percent

Drawbacks

• Uses memory to load files
  • 16 bytes per line in bedGraph file
  • 4 bytes per basePair in every chromosome loaded
• Loading the bedGraph file can take up to a minute or two
• Only works with sorted bedgraph files

Installation

Dependency requirements:

• Numpy >= v1.16.4
• pyBigWig >= v0.3.16 (For reading bigWig files)
  • pyBigWig == 0.3.16 (For Benchmarking)

With pip:

pip3 install pyBedGraph

With conda:

conda create -n test
conda activate test
conda install -c bioconda pyBedGraph

Usage

Download the test files here:

https://thejacksonlaboratory.ent.box.com/s/3jglutwf3d54pnomnp33ivo7a9546vhe

Test files are also included in this Github repository: test/test_files.

Enter the directory with the test files.

Create the object:

from pyBedGraph import BedGraph

# arg1 - chromosome sizes file
# arg2 - bedgraph file
# arg3 - (optional) chromosome_name
# Just load chromosome 'chr1' (uses less memory and takes less time)
bedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph', 'chr1')

# Load the whole bedGraph file
bedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph')

# Option to not ignore missing basePairs when calculating statistics
# Used the exact same way but produces slightly different results
inclusive_bedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph', ignore_missing_bp=False)

Choose and load a chromosome to search for:

bedGraph.load_chrom_data('chr1')
inclusive_bedGraph.load_chrom_data('chr1')

Load bins for finding mean:

For approx_mean:

1. Smaller bin size -> more accurate but slower
2. Larger bin size -> less accurate but faster

bedGraph.load_chrom_bins('chr1', 3)
inclusive_bedGraph.load_chrom_bins('chr1', 3)

Choose a specific statistic to search for:

• 'mean'
• 'approx_mean' - an approximate mean is faster than exact mean, with < 5% error rate
• 'max'
• 'min'
• 'coverage'
• 'std' - (population standard deviation)

Search from a list of intervals:

import numpy as np

# Option 1
test_intervals = [
    ['chr1', 24, 26],
    ['chr1', 12, 15],
    ['chr1', 8, 12],
    ['chr1', 9, 10],
    ['chr1', 0, 5]
]
values = bedGraph.stats(intervals=test_intervals)

# [-1.    0.9   0.1  -1.    0.82]
print(values)

# Option 2
start_list = np.array([24, 12, 8, 9, 0], dtype=np.int32)
end_list = np.array([26, 15, 12, 10, 5], dtype=np.int32)
chrom_name = 'chr1'

# arg1 - (optional) stat (default is 'mean')
# arg2 - intervals
# arg3 - start_list
# arg4 - end_list
# arg5 - chrom_name
# must have either intervals or start_list, end_list, chrom_name
# returns a numpy array of values
result = bedGraph.stats(start_list=start_list, end_list=end_list, chrom_name=chrom_name)

# [-1.    0.9   0.1  -1.    0.82]
print(result)

Search from a file:

# arg1 - interval file
# arg2 - (optional) output_to_file (default is True and outputs to 'chr1_out.txt'
# arg3 - (optional) stat (default is 'mean')
# returns a dictionary; keys are chromosome names, values are numpy arrays
result = bedGraph.stats_from_file('test_intervals.txt', output_to_file=False, stat='mean')

# {'chr1': array([-1.  ,  0.9 ,  0.1 , -1.  ,  0.82])}
print(result)

Sample Tests (from included test files):

# [-1.    0.9   0.1  -1.    0.82]
bedGraph.stats('mean', test_intervals)

# [-1.          0.9        0.1.         -1.          0.8076923076923077]
bedGraph.stats('approx_mean', test_intervals)

# [0.         0.33333333 0.25       0.         1.        ]
bedGraph.stats('coverage', test_intervals)

# [-1.   0.9  0.1 -1.   0.7]
bedGraph.stats('min', test_intervals)

# [-1.   0.9  0.1 -1.   0.9]
bedGraph.stats('max', test_intervals)

# [-1.          0.          0.         -1.          0.09797959]
bedGraph.stats('std', test_intervals)

# [0.    0.3   0.025 0.    0.82 ]
inclusive_bedGraph.stats('mean', test_intervals)

# [0.         0.3        0.00833333 0.         0.7       ]
inclusive_bedGraph.stats('approx_mean', test_intervals)

# [0.         0.33333333 0.25       0.         1.        ]
inclusive_bedGraph.stats('coverage', test_intervals)

# [0.  0.  0.1 0.  0.7]
inclusive_bedGraph.stats('min', test_intervals)

# [0.  0.9 0.1 0.  0.9]
inclusive_bedGraph.stats('max', test_intervals)

# [0.         0.42426407 0.04330127 0.         0.09797959]
inclusive_bedGraph.stats('std', test_intervals)

Benchmarking pyBedGraph:

Actual values are found from the stats function in pyBigWig with the exact argument being True. The error for exact stats will be ~1e-8 due to rounding error of conversion of bigWig and bedGraph files.

Alternatively, one can make actual values be pyBedGraph's exact statistics.

Enter the graphs folder in the Github project repository.

from pyBedGraph import BedGraph
from Benchmark import Benchmark

# These files can be downloaded from the link given above
bedGraph = BedGraph('mm10.chrom.sizes', 'ENCFF376VCU.bedGraph', 'chr1')

# Alternatively using a bigwig file
# bedGraph = BedGraph('mm10.chrom.sizes', 'ENCFF376VCU.bigWig', 'chr1')

bedGraph.load_chrom_data('chr1')
bedGraph.load_chrom_bins('chr1', 100)

# arg1 - BedGraph object
# arg2 - bigwig file
bench = Benchmark(bedGraph, 'ENCFF376VCU.bigWig')

# arg1 - num_tests
# arg2 - interval_size
# arg3 - chrom_nam
# arg4 - bin_size
# arg5 - stats (optional) (Default is all stats)
# arg6 - just_runtime (optional) (Default is False)
# arg6 - bench_pyBigWig_approx (optional) (Default is True)
# arg6 - make_pyBigWig_baseline (optional) (Default is True)
# Test all statistics
result = bench.benchmark(10000, 5000, 'chr1', 100)

for key in result:
    print(key, result[key])

# mean {'run_time': 0.008324861526489258, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}
# pyBigWig_mean {'approx_run_time': 1.4333949089050293, 'exact_run_time': 0.7698564529418945, 'error': {'percent_error': 0.06567272540694802, 'ms_error': 0.001222419386871348, 'abs_error': 0.023540340949669364, 'not_included': 79}}
# approx_mean {'run_time': 0.002111673355102539, 'error': {'percent_error': 0.006529644707171326, 'ms_error': 7.858080037556034e-06, 'abs_error': 0.001824641073039555, 'not_included': 4}}
# max {'run_time': 0.005040645599365234, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}
# pyBigWig_max {'approx_run_time': 1.2673799991607666, 'exact_run_time': 0.7933700084686279, 'error': {'percent_error': 0.10220448242023446, 'ms_error': 1.2678718593032368, 'abs_error': 0.25865022624731066, 'not_included': 79}}
# min {'run_time': 0.005083560943603516, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}
# pyBigWig_min {'approx_run_time': 1.2120039463043213, 'exact_run_time': 0.7468140125274658, 'error': {'percent_error': 0.0001, 'ms_error': 7.109862619931795e-07, 'abs_error': 8.432000130414962e-06, 'not_included': 0}}
# coverage {'run_time': 0.0063626766204833984, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}
# pyBigWig_coverage {'approx_run_time': 1.2101118564605713, 'exact_run_time': 0.7483360767364502, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}
# std {'run_time': 0.0422673225402832, 'error': {'percent_error': 9.690484548456011e-05, 'ms_error': 4.764358150024449e-09, 'abs_error': 6.25265457158463e-05, 'not_included': 0}}
# pyBigWig_std {'approx_run_time': 1.219078540802002, 'exact_run_time': 0.7484426498413086, 'error': {'percent_error': 0.04560011737269686, 'ms_error': 0.005008324729263816, 'abs_error': 0.02569405301725115, 'not_included': 79}}

Reference

pyBedGraph: a Python package for fast operations on 1-dimensional genomic signal tracks, Zhang et al., bioRxiv, 2019

Bug reports

To report bugs, contact Henry ([e-mail]) and Minji ([e-mail]) or visit the Issues page.