| Name | pyranges JSON |
| Version |
0.0.129
JSON |
| download |
| home_page | |
| Summary | GenomicRanges for Python. |
| upload_time | 2023-06-29 14:17:10 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | MIT |
| keywords |
bioinformatics
genomicranges
genomics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# pyranges
[](https://coveralls.io/github/biocore-ntnu/pyranges?branch=master) [](https://www.codacy.com/app/endrebak/pyranges?utm_source=github.com&utm_medium=referral&utm_content=biocore-ntnu/pyranges&utm_campaign=Badge_Grade) [](https://travis-ci.com/biocore-ntnu/pyranges) [](http://hypothesis.readthedocs.io/) [](https://badge.fury.io/py/pyranges) [](https://opensource.org/licenses/MIT)  [](http://bioconda.github.io/recipes/pyranges/README.html)
## Introduction
GenomicRanges and genomic Rle-objects for Python.
*"Finally ... This was what Python badly needed for years."* - Heng Li
## Release
PyRanges is in a beta state until any possible issues with the move to pandas
1.0 have been ironed out. We are extremely responsive to bug-reports, so if you
have problems or come across unexpected behavior, please create an issue.
## Asking for help
Feel free to ask questions of the type "how do I do X with pyranges?" on public forums like Stack Overflow, Bioinformatics Stack Exchange or Biostars. You can use endrebak85#gmail.com to point me to the question(s).
## Quick examples
```python
import pyranges as pr
# load example datasets
exons, cpg = pr.data.exons(), pr.data.cpg()
# subsetting pyranges is easy
exons["chrY", "-", 15591259:27197945]
# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+
# | Chromosome | Start | End | Name | Score | Strand |
# | (category) | (int32) | (int32) | (object) | (int64) | (category) |
# |--------------|-----------|-----------|----------------------------------------|-----------|--------------|
# | chrY | 15591393 | 15592550 | NR_047610_exon_27_0_chrY_15591394_r | 0 | - |
# | chrY | 15591393 | 15592550 | NR_047607_exon_29_0_chrY_15591394_r | 0 | - |
# | chrY | 15591393 | 15592550 | NM_001258269_exon_29_0_chrY_15591394_r | 0 | - |
# | chrY | 15591393 | 15592550 | NR_047599_exon_28_0_chrY_15591394_r | 0 | - |
# | ... | ... | ... | ... | ... | ... |
# | chrY | 25336491 | 25336631 | NM_004081_exon_22_0_chrY_25336492_r | 0 | - |
# | chrY | 26952215 | 26952307 | NM_020364_exon_16_0_chrY_26952216_r | 0 | - |
# | chrY | 27197822 | 27197945 | NM_004678_exon_7_0_chrY_27197823_r | 0 | - |
# | chrY | 27197822 | 27197945 | NM_001002760_exon_7_0_chrY_27197823_r | 0 | - |
# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+
# Stranded PyRanges object has 22 rows and 6 columns from 1 chromosomes.
# you can use your pandas-skills with pyranges
exons[~exons.Name.str.startswith("NR")] # all rows where the name column does not start with "NR"
# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+
# | Chromosome | Start | End | Name | Score | Strand |
# | (category) | (int32) | (int32) | (object) | (int64) | (category) |
# |--------------|-----------|-----------|----------------------------------------|-----------|--------------|
# | chrX | 135574120 | 135574598 | NM_001727_exon_2_0_chrX_135574121_f | 0 | + |
# | chrX | 47868945 | 47869126 | NM_205856_exon_4_0_chrX_47868946_f | 0 | + |
# | chrX | 77294333 | 77294480 | NM_000052_exon_17_0_chrX_77294334_f | 0 | + |
# | chrX | 91090459 | 91091043 | NM_001168360_exon_0_0_chrX_91090460_f | 0 | + |
# | ... | ... | ... | ... | ... | ... |
# | chrY | 15481135 | 15481229 | NM_182659_exon_16_0_chrY_15481136_r | 0 | - |
# | chrY | 25325872 | 25325936 | NM_004081_exon_18_0_chrY_25325873_r | 0 | - |
# | chrY | 15560896 | 15560946 | NM_001258258_exon_25_0_chrY_15560897_r | 0 | - |
# | chrY | 15467254 | 15467278 | NM_001258270_exon_13_0_chrY_15467255_r | 0 | - |
# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+
# Stranded PyRanges object has 847 rows and 6 columns from 2 chromosomes.
# the API allows for easy and terse chaining
(cpg # use the cpg dataset
.join(exons, suffix="_xn") # join with exons, use suffix _xn for duplicate cols
.subset(lambda df: df.CpG > 30) # keep only rows with a CpG score over 30
.sort(nb_cpu=2) # sort on Chromosome, Start and End
# note that virtually all pyranges-methods take a nb_cpu argument
# to use multiple cores, you need to install ray with pip install ray
.pc(formatting={"Start": "{:,}", "End": "{:,}", "Name": "{:2.2}"})
# print, while keeping the chain (c) going. Try .sp(), msp(), rp(), spc(), mspc() also :)
["chrX"] # keep only chromosome X
.assign("CpGDecile", lambda df: df.CpG / 10) # Insert new column
.unstrand()) # remove the strand info
# +--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------+
# | Chromosome | Start | End | CpG | Start_xn | End_xn | Name | Score | Strand |
# | (category) | (int32) | (int32) | (int64) | (int32) | (int32) | (object) | (int64) | (category) |
# |--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------|
# | chrX | 584,563 | 585,326 | 66 | 585078 | 585337 | NM | 0 | + |
# | chrX | 1,510,501 | 1,511,838 | 173 | 1510791 | 1511039 | NM | 0 | - |
# | chrX | 2,846,195 | 2,847,511 | 92 | 2847272 | 2847416 | NM | 0 | - |
# | chrX | 13,587,648 | 13,588,221 | 49 | 13587693 | 13588054 | NM | 0 | + |
# | ... | ... | ... | ... | ... | ... | ... | ... | ... |
# | chrX | 153,068,787 | 153,070,353 | 134 | 153067622 | 153070355 | NM | 0 | - |
# | chrX | 153,284,685 | 153,285,655 | 94 | 153284647 | 153284779 | NM | 0 | - |
# | chrX | 153,598,874 | 153,600,604 | 164 | 153599240 | 153599729 | NM | 0 | - |
# | chrX | 153,990,840 | 153,991,831 | 105 | 153991030 | 153991256 | NM | 0 | + |
# +--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------+
# Unstranded PyRanges object has 65 rows and 9 columns from 2 chromosomes.
# +--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------+
# | Chromosome | Start | End | CpG | Start_xn | End_xn | Name | Score | CpGDecile |
# | (category) | (int32) | (int32) | (int64) | (int32) | (int32) | (object) | (int64) | (float64) |
# |--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------|
# | chrX | 584563 | 585326 | 66 | 585078 | 585337 | NM_000451_exon_0_0_chrX_585079_f | 0 | 6.6 |
# | chrX | 1510501 | 1511838 | 173 | 1510791 | 1511039 | NM_001636_exon_3_0_chrX_1510792_r | 0 | 17.3 |
# | chrX | 2846195 | 2847511 | 92 | 2847272 | 2847416 | NM_001669_exon_9_0_chrX_2847273_r | 0 | 9.2 |
# | chrX | 13587648 | 13588221 | 49 | 13587693 | 13588054 | NM_001167890_exon_0_0_chrX_13587694_f | 0 | 4.9 |
# | ... | ... | ... | ... | ... | ... | ... | ... | ... |
# | chrX | 153068787 | 153070353 | 134 | 153067622 | 153070355 | NM_032512_exon_0_0_chrX_153067623_r | 0 | 13.4 |
# | chrX | 153284685 | 153285655 | 94 | 153284647 | 153284779 | NM_001025243_exon_10_0_chrX_153284648_r | 0 | 9.4 |
# | chrX | 153598874 | 153600604 | 164 | 153599240 | 153599729 | NM_001456_exon_45_0_chrX_153599241_r | 0 | 16.4 |
# | chrX | 153990840 | 153991831 | 105 | 153991030 | 153991256 | NM_001363_exon_0_0_chrX_153991031_f | 0 | 10.5 |
# +--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------+
# Unstranded PyRanges object has 58 rows and 9 columns from 1 chromosomes.
cpg_rle = cpg.to_rle(value_col="CpG") # ignore value_col for regular coverage
cpg_rle
# chrX
# ----
# +--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------+
# | Runs | 64181 | 612 | 4340 | 896 | 78656 | ... | 607 | 268069 | 389 | 135083 | 308 |
# |--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------|
# | Values | 0.0 | 62.0 | 0.0 | 100.0 | 0.0 | ... | 44.0 | 0.0 | 36.0 | 0.0 | 29.0 |
# +--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------+
# Rle of length 155246568 containing 1792 elements
#
# chrY
# ----
# +--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------+
# | Runs | 14181 | 612 | 4340 | 896 | 78656 | ... | 229 | 30440250 | 389 | 135083 | 308 |
# |--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------|
# | Values | 0.0 | 62.0 | 0.0 | 100.0 | 0.0 | ... | 25.0 | 0.0 | 36.0 | 0.0 | 29.0 |
# +--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------+
# Rle of length 59349574 containing 362 elements
# Unstranded PyRles object with 2 chromosomes.
```
## Features
- fast (also in single-core mode)
- supports multiple cores
- memory-efficient
- featureful
- pythonic/pandastic
- supports chaining with a terse syntax
- uses Pandas DataFrames, so the whole Python data science stack works on PyRanges.
## Supporting pyranges
- most importantly, cite pyranges if you use it. It is the main metric funding sources care about.
- use pyranges in Stack Overflow/biostars questions and answers
- star the repo (possibly important for github visibility and as a proxy for project popularity)
- if you are a business using pyranges, please give to one of the charities listed at https://www.givewell.org/
## Documentation
<https://biocore-ntnu.github.io/pyranges/>
(Might be slightly out of date; watch the CHANGELOG too)
## Install
The preferred way to install pyranges is through the bioconda channel:
```bash
conda install -c bioconda pyranges
```
You can also try pip:
```bash
pip install pyranges
```
PyRanges has some dependencies that are optional. They need to be manually installed if
you require their functionality:
```
ray: multicpu # pip install -U ray
pybigwig: write bigwigs # pip install pybigwig or conda install -c bioconda pybigwig
bamread: read bam files # pip install bamread or conda install -c bioconda bamread
fisher: fast fisher exact # pip install fisher or conda install -c bioconda fisher
```
Since these are not needed for 99.9% percent of the pyranges functionality, they
are kept separate to prevent the possibility of the pyranges-install failing due
to dependencies that fail installation or conflicting dependencies.
## Paper/Cite
http://dx.doi.org/10.1093/bioinformatics/btz615
## TODO
For the future:
- write docstrings, autogenerate API-docs
PyRanges should always be the fastest general-purpose genomics library for
Python. So I will happily change the multithreading library and overlap
datastructures sometime in the future, if rigorous tests show that the proposed
alternatives are indeed faster. (As the multithreading requires about 30 lines
of code and the overlap queries about 15, this will not be hard.)
## Performance
<img src="./graphs/main_paper_annotation_binary.png" />
Comprehensive set of graphs for many types of functions on different datasets are here:
[Time](https://github.com/endrebak/pyranges-paper/blob/master/supplementary_paper/time.md)
[Memory](https://github.com/endrebak/pyranges-paper/blob/master/supplementary_paper/memory.md)
The exact code tested is found [here](https://github.com/endrebak/pyranges-paper/tree/master/supplementaries).
Raw data
{
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"keywords": "bioinformatics,genomicranges,genomics",
"author": "",
"author_email": "Endre Bakken Stovner <endbak@pm.me>",
"download_url": "https://files.pythonhosted.org/packages/e2/a6/a68a65a8f5fd87064d9a8e22354245f11ddee60e1e699b7fa5902001cf0c/pyranges-0.0.129.tar.gz",
"platform": null,
"description": "# pyranges\n\n[](https://coveralls.io/github/biocore-ntnu/pyranges?branch=master) [](https://www.codacy.com/app/endrebak/pyranges?utm_source=github.com&utm_medium=referral&utm_content=biocore-ntnu/pyranges&utm_campaign=Badge_Grade) [](https://travis-ci.com/biocore-ntnu/pyranges) [](http://hypothesis.readthedocs.io/) [](https://badge.fury.io/py/pyranges) [](https://opensource.org/licenses/MIT)  [](http://bioconda.github.io/recipes/pyranges/README.html)\n\n## Introduction\n\nGenomicRanges and genomic Rle-objects for Python.\n\n*\"Finally ... This was what Python badly needed for years.\"* - Heng Li\n\n## Release\n\nPyRanges is in a beta state until any possible issues with the move to pandas\n1.0 have been ironed out. We are extremely responsive to bug-reports, so if you\nhave problems or come across unexpected behavior, please create an issue.\n\n## Asking for help\n\nFeel free to ask questions of the type \"how do I do X with pyranges?\" on public forums like Stack Overflow, Bioinformatics Stack Exchange or Biostars. You can use endrebak85#gmail.com to point me to the question(s).\n\n## Quick examples\n\n```python\nimport pyranges as pr\n# load example datasets\nexons, cpg = pr.data.exons(), pr.data.cpg()\n\n# subsetting pyranges is easy\nexons[\"chrY\", \"-\", 15591259:27197945]\n# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+\n# | Chromosome | Start | End | Name | Score | Strand |\n# | (category) | (int32) | (int32) | (object) | (int64) | (category) |\n# |--------------|-----------|-----------|----------------------------------------|-----------|--------------|\n# | chrY | 15591393 | 15592550 | NR_047610_exon_27_0_chrY_15591394_r | 0 | - |\n# | chrY | 15591393 | 15592550 | NR_047607_exon_29_0_chrY_15591394_r | 0 | - |\n# | chrY | 15591393 | 15592550 | NM_001258269_exon_29_0_chrY_15591394_r | 0 | - |\n# | chrY | 15591393 | 15592550 | NR_047599_exon_28_0_chrY_15591394_r | 0 | - |\n# | ... | ... | ... | ... | ... | ... |\n# | chrY | 25336491 | 25336631 | NM_004081_exon_22_0_chrY_25336492_r | 0 | - |\n# | chrY | 26952215 | 26952307 | NM_020364_exon_16_0_chrY_26952216_r | 0 | - |\n# | chrY | 27197822 | 27197945 | NM_004678_exon_7_0_chrY_27197823_r | 0 | - |\n# | chrY | 27197822 | 27197945 | NM_001002760_exon_7_0_chrY_27197823_r | 0 | - |\n# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+\n# Stranded PyRanges object has 22 rows and 6 columns from 1 chromosomes.\n\n# you can use your pandas-skills with pyranges\nexons[~exons.Name.str.startswith(\"NR\")] # all rows where the name column does not start with \"NR\"\n# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+\n# | Chromosome | Start | End | Name | Score | Strand |\n# | (category) | (int32) | (int32) | (object) | (int64) | (category) |\n# |--------------|-----------|-----------|----------------------------------------|-----------|--------------|\n# | chrX | 135574120 | 135574598 | NM_001727_exon_2_0_chrX_135574121_f | 0 | + |\n# | chrX | 47868945 | 47869126 | NM_205856_exon_4_0_chrX_47868946_f | 0 | + |\n# | chrX | 77294333 | 77294480 | NM_000052_exon_17_0_chrX_77294334_f | 0 | + |\n# | chrX | 91090459 | 91091043 | NM_001168360_exon_0_0_chrX_91090460_f | 0 | + |\n# | ... | ... | ... | ... | ... | ... |\n# | chrY | 15481135 | 15481229 | NM_182659_exon_16_0_chrY_15481136_r | 0 | - |\n# | chrY | 25325872 | 25325936 | NM_004081_exon_18_0_chrY_25325873_r | 0 | - |\n# | chrY | 15560896 | 15560946 | NM_001258258_exon_25_0_chrY_15560897_r | 0 | - |\n# | chrY | 15467254 | 15467278 | NM_001258270_exon_13_0_chrY_15467255_r | 0 | - |\n# +--------------|-----------|-----------|----------------------------------------|-----------|--------------+\n# Stranded PyRanges object has 847 rows and 6 columns from 2 chromosomes.\n\n# the API allows for easy and terse chaining\n(cpg # use the cpg dataset\n .join(exons, suffix=\"_xn\") # join with exons, use suffix _xn for duplicate cols\n .subset(lambda df: df.CpG > 30) # keep only rows with a CpG score over 30\n .sort(nb_cpu=2) # sort on Chromosome, Start and End\n # note that virtually all pyranges-methods take a nb_cpu argument\n # to use multiple cores, you need to install ray with pip install ray\n .pc(formatting={\"Start\": \"{:,}\", \"End\": \"{:,}\", \"Name\": \"{:2.2}\"})\n # print, while keeping the chain (c) going. Try .sp(), msp(), rp(), spc(), mspc() also :)\n [\"chrX\"] # keep only chromosome X\n .assign(\"CpGDecile\", lambda df: df.CpG / 10) # Insert new column\n .unstrand()) # remove the strand info\n# +--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------+\n# | Chromosome | Start | End | CpG | Start_xn | End_xn | Name | Score | Strand |\n# | (category) | (int32) | (int32) | (int64) | (int32) | (int32) | (object) | (int64) | (category) |\n# |--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------|\n# | chrX | 584,563 | 585,326 | 66 | 585078 | 585337 | NM | 0 | + |\n# | chrX | 1,510,501 | 1,511,838 | 173 | 1510791 | 1511039 | NM | 0 | - |\n# | chrX | 2,846,195 | 2,847,511 | 92 | 2847272 | 2847416 | NM | 0 | - |\n# | chrX | 13,587,648 | 13,588,221 | 49 | 13587693 | 13588054 | NM | 0 | + |\n# | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n# | chrX | 153,068,787 | 153,070,353 | 134 | 153067622 | 153070355 | NM | 0 | - |\n# | chrX | 153,284,685 | 153,285,655 | 94 | 153284647 | 153284779 | NM | 0 | - |\n# | chrX | 153,598,874 | 153,600,604 | 164 | 153599240 | 153599729 | NM | 0 | - |\n# | chrX | 153,990,840 | 153,991,831 | 105 | 153991030 | 153991256 | NM | 0 | + |\n# +--------------|-------------|-------------|-----------|------------|-----------|------------|-----------|--------------+\n# Unstranded PyRanges object has 65 rows and 9 columns from 2 chromosomes.\n# +--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------+\n# | Chromosome | Start | End | CpG | Start_xn | End_xn | Name | Score | CpGDecile |\n# | (category) | (int32) | (int32) | (int64) | (int32) | (int32) | (object) | (int64) | (float64) |\n# |--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------|\n# | chrX | 584563 | 585326 | 66 | 585078 | 585337 | NM_000451_exon_0_0_chrX_585079_f | 0 | 6.6 |\n# | chrX | 1510501 | 1511838 | 173 | 1510791 | 1511039 | NM_001636_exon_3_0_chrX_1510792_r | 0 | 17.3 |\n# | chrX | 2846195 | 2847511 | 92 | 2847272 | 2847416 | NM_001669_exon_9_0_chrX_2847273_r | 0 | 9.2 |\n# | chrX | 13587648 | 13588221 | 49 | 13587693 | 13588054 | NM_001167890_exon_0_0_chrX_13587694_f | 0 | 4.9 |\n# | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n# | chrX | 153068787 | 153070353 | 134 | 153067622 | 153070355 | NM_032512_exon_0_0_chrX_153067623_r | 0 | 13.4 |\n# | chrX | 153284685 | 153285655 | 94 | 153284647 | 153284779 | NM_001025243_exon_10_0_chrX_153284648_r | 0 | 9.4 |\n# | chrX | 153598874 | 153600604 | 164 | 153599240 | 153599729 | NM_001456_exon_45_0_chrX_153599241_r | 0 | 16.4 |\n# | chrX | 153990840 | 153991831 | 105 | 153991030 | 153991256 | NM_001363_exon_0_0_chrX_153991031_f | 0 | 10.5 |\n# +--------------|-----------|-----------|-----------|------------|-----------|-----------------------------------------|-----------|-------------+\n# Unstranded PyRanges object has 58 rows and 9 columns from 1 chromosomes.\n\ncpg_rle = cpg.to_rle(value_col=\"CpG\") # ignore value_col for regular coverage\ncpg_rle\n# chrX\n# ----\n# +--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------+\n# | Runs | 64181 | 612 | 4340 | 896 | 78656 | ... | 607 | 268069 | 389 | 135083 | 308 |\n# |--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------|\n# | Values | 0.0 | 62.0 | 0.0 | 100.0 | 0.0 | ... | 44.0 | 0.0 | 36.0 | 0.0 | 29.0 |\n# +--------|---------|-------|--------|-------|---------|---------|-------|----------|-------|----------|-------+\n# Rle of length 155246568 containing 1792 elements\n#\n# chrY\n# ----\n# +--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------+\n# | Runs | 14181 | 612 | 4340 | 896 | 78656 | ... | 229 | 30440250 | 389 | 135083 | 308 |\n# |--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------|\n# | Values | 0.0 | 62.0 | 0.0 | 100.0 | 0.0 | ... | 25.0 | 0.0 | 36.0 | 0.0 | 29.0 |\n# +--------|---------|-------|--------|-------|---------|---------|-------|------------|-------|----------|-------+\n# Rle of length 59349574 containing 362 elements\n# Unstranded PyRles object with 2 chromosomes.\n\n```\n\n## Features\n\n - fast (also in single-core mode)\n - supports multiple cores\n - memory-efficient\n - featureful\n - pythonic/pandastic\n - supports chaining with a terse syntax\n - uses Pandas DataFrames, so the whole Python data science stack works on PyRanges.\n\n## Supporting pyranges\n\n- most importantly, cite pyranges if you use it. It is the main metric funding sources care about.\n- use pyranges in Stack Overflow/biostars questions and answers\n- star the repo (possibly important for github visibility and as a proxy for project popularity)\n- if you are a business using pyranges, please give to one of the charities listed at https://www.givewell.org/\n\n## Documentation\n\n<https://biocore-ntnu.github.io/pyranges/>\n\n(Might be slightly out of date; watch the CHANGELOG too)\n\n## Install\n\nThe preferred way to install pyranges is through the bioconda channel:\n\n```bash\nconda install -c bioconda pyranges\n```\n\nYou can also try pip:\n\n```bash\npip install pyranges\n```\n\nPyRanges has some dependencies that are optional. They need to be manually installed if\nyou require their functionality:\n\n```\nray: multicpu # pip install -U ray\npybigwig: write bigwigs # pip install pybigwig or conda install -c bioconda pybigwig\nbamread: read bam files # pip install bamread or conda install -c bioconda bamread\nfisher: fast fisher exact # pip install fisher or conda install -c bioconda fisher\n```\n\nSince these are not needed for 99.9% percent of the pyranges functionality, they\nare kept separate to prevent the possibility of the pyranges-install failing due\nto dependencies that fail installation or conflicting dependencies.\n\n## Paper/Cite\n\nhttp://dx.doi.org/10.1093/bioinformatics/btz615\n\n## TODO\n\nFor the future:\n\n - write docstrings, autogenerate API-docs\n\n\nPyRanges should always be the fastest general-purpose genomics library for\nPython. So I will happily change the multithreading library and overlap\ndatastructures sometime in the future, if rigorous tests show that the proposed\nalternatives are indeed faster. (As the multithreading requires about 30 lines\nof code and the overlap queries about 15, this will not be hard.)\n\n## Performance\n\n<img src=\"./graphs/main_paper_annotation_binary.png\" />\n\nComprehensive set of graphs for many types of functions on different datasets are here:\n\n[Time](https://github.com/endrebak/pyranges-paper/blob/master/supplementary_paper/time.md)\n\n[Memory](https://github.com/endrebak/pyranges-paper/blob/master/supplementary_paper/memory.md)\n\nThe exact code tested is found [here](https://github.com/endrebak/pyranges-paper/tree/master/supplementaries).\n",
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