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Frequently used commands in bioinformatics

Project description

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Introduction

The main goal of the fuc package is to wrap some of the most frequently used commands in the field of bioinformatics into one place.

You can use fuc for both command line interface (CLI) and application programming interface (API) whose documentations are available at Read the Docs.

Currently, the following file formats are supported by fuc:

  • Sequence Alignment/Map (SAM)
  • Binary Alignment/Map (BAM)
  • CRAM
  • Variant Call Format (VCF)
  • Mutation Annotation Format (MAF)
  • Browser Extensible Data (BED)
  • FASTQ
  • delimiter-separated values format (e.g. comma-separated values or CSV format)

Additionally, you can use fuc to parse output data from the following programs:

  • Ensembl Variant Effect Predictor (VEP)
  • SnpEff
  • bcl2fastq and bcl2fastq2

Your contributions (e.g. feature ideas, pull requests) are most welcome.

Author: Seung-been “Steven” Lee
License: MIT License

CLI Examples

SAM/BAM/CRAM

To print the header of a BAM file:

$ fuc bam_head example.bam

BED

To find intersection between BED files:

$ fuc bed_intxn 1.bed 2.bed 3.bed > intersect.bed

FASTQ

To count sequence reads in a FASTQ file:

$ fuc fq_count example.fastq

FUC

To check whether a file exists in the operating system:

$ fuc fuc_exist example.txt

To find all VCF files within the current directory recursively:

$ fuc fuc_find . vcf

TABLE

To merge two tab-delimited files:

$ fuc tbl_merge left.txt right.txt > merged.txt

VCF

To merge VCF files:

$ fuc vcf_merge 1.vcf 2.vcf 3.vcf > merged.vcf

To filter a VCF file annotated by Ensemble VEP:

$ fuc vcf_vep in.vcf 'SYMBOL == "TP53"' > out.vcf

API Examples

VCF

To filter a VCF file based on a BED file:

>>> from fuc import pyvcf
>>> vf = pyvcf.VcfFrame.from_file('original.vcf')
>>> filtered_vf = vf.filter_bed('targets.bed')
>>> filtered_vf.to_file('filtered.vcf')

To remove indels from a VCF file:

>>> from fuc import pyvcf
>>> vf = pyvcf.VcfFrame.from_file('with_indels.vcf')
>>> filtered_vf = vf.filter_indel()
>>> filtered_vf.to_file('no_indels.vcf')

To create a Venn diagram showing genotype concordance between groups:

>>> from fuc import pyvcf, common
>>> common.load_dataset('pyvcf')
>>> f = '~/fuc-data/pyvcf/plot_comparison.vcf'
>>> vf = pyvcf.VcfFrame.from_file(f)
>>> a = ['Steven_A', 'John_A', 'Sara_A']
>>> b = ['Steven_B', 'John_B', 'Sara_B']
>>> c = ['Steven_C', 'John_C', 'Sara_C']
>>> vf.plot_comparison(a, b, c)
https://raw.githubusercontent.com/sbslee/fuc-data/main/images/plot_comparison.png

To create various figures for normal-tumor analysis:

>>> import matplotlib.pyplot as plt
>>> from fuc import common, pyvcf
>>> common.load_dataset('pyvcf')
>>> vf = pyvcf.VcfFrame.from_file('~/fuc-data/pyvcf/normal-tumor.vcf')
>>> af = pyvcf.AnnFrame.from_file('~/fuc-data/pyvcf/normal-tumor-annot.tsv', 'Sample')
>>> normal = af.df[af.df.Tissue == 'Normal'].index
>>> tumor = af.df[af.df.Tissue == 'Tumor'].index
>>> fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, figsize=(10, 10))
>>> vf.plot_tmb(ax=ax1)
>>> vf.plot_tmb(ax=ax2, af=af, hue='Tissue')
>>> vf.plot_hist('DP', ax=ax3, af=af, hue='Tissue')
>>> vf.plot_regplot(normal, tumor, ax=ax4)
>>> plt.tight_layout()
https://raw.githubusercontent.com/sbslee/fuc-data/main/images/normal-tumor.png

MAF

To create an oncoplot with a MAF file:

>>> from fuc import common, pymaf
>>> common.load_dataset('tcga-laml')
>>> f = '~/fuc-data/tcga-laml/tcga_laml.maf.gz'
>>> mf = pymaf.MafFrame.from_file(f)
>>> mf.plot_oncoplot()
https://raw.githubusercontent.com/sbslee/fuc-data/main/images/oncoplot.png

To create a customized oncoplot with a MAF file, see the ‘Create customized oncoplot’ tutorial:

https://raw.githubusercontent.com/sbslee/fuc-data/main/images/customized_oncoplot.png

To create a summary figure for a MAF file:

>>> from fuc import common, pymaf
>>> common.load_dataset('tcga-laml')
>>> f = '~/fuc-data/tcga-laml/tcga_laml.maf.gz'
>>> mf = pymaf.MafFrame.from_file(f)
>>> mf.plot_summary()
https://raw.githubusercontent.com/sbslee/fuc-data/main/images/maf_summary.png

SAM/BAM/CRAM

To create read depth profile of a region from a CRAM file:

>>> from fuc import pycov
>>> cf = pycov.CovFrame.from_file('HG00525.final.cram', zero=True,
...    region='chr12:21161194-21239796', names=['HG00525'])
>>> cf.plot_region('chr12', start=21161194, end=21239796)
https://raw.githubusercontent.com/sbslee/fuc-data/main/images/coverage.png

Installation

The following packages are required to run fuc:

biopython
lxml
matplotlib
matplotlib-venn
numpy
pandas
pyranges
pysam
scipy
seaborn

There are various ways you can install fuc. The recommended way is via conda:

$ conda install -c bioconda fuc

Above will automatically download and install all the dependencies as well. Alternatively, you can use pip to install fuc and all of its dependencies:

$ pip install fuc

Finally, you can clone the GitHub repository and then install fuc this way:

$ git clone https://github.com/sbslee/fuc
$ cd fuc
$ pip install .

The nice thing about this approach is that you will have access to development versions that are not available in Anaconda or PyPI. For example, you can access a development branch with the git checkout command. When you do this, please make sure your environment already has all the dependencies installed.

Getting Help

For detailed documentations on fuc’s CLI and API, please refer to the Read the Docs.

For getting help on CLI:

$ fuc -h
usage: fuc [-h] [-v] COMMAND ...

positional arguments:
  COMMAND        name of the command
    bam_head     [BAM] print the header of a BAM file
    bam_index    [BAM] index a BAM file
    bam_rename   [BAM] add a new sample name to a BAM file
    bam_slice    [BAM] slice a BAM file
    bed_intxn    [BED] find intersection of two or more BED files
    bed_sum      [BED] summarize a BED file
    fq_count     [FASTQ] count sequence reads in FASTQ files
    fq_sum       [FASTQ] summarize a FASTQ file
    fuc_compf    [FUC] compare contents of two files
    fuc_demux    [FUC] parse Reports directory from bcl2fastq or bcl2fastq2
    fuc_exist    [FUC] check whether files/dirs exist
    fuc_find     [FUC] find files with certain extension recursively
    maf_oncoplt  [MAF] create an oncoplot from a MAF file
    maf_sumplt   [MAF] create a summary plot for a MAF file
    maf_vcf2maf  [MAF] convert an annotated VCF file to a MAF file
    tbl_merge    [TABLE] merge two table files
    tbl_sum      [TABLE] summarize a table file
    vcf_merge    [VCF] merge two or more VCF files
    vcf_slice    [VCF] slice a VCF file
    vcf_vcf2bed  [VCF] convert a VCF file to a BED file
    vcf_vep      [VCF] filter a VCF file annotated by Ensemble VEP

optional arguments:
  -h, --help     show this help message and exit
  -v, --version  show the version number and exit

For getting help on a specific command (e.g. vcf_merge):

$ fuc vcf_merge -h

Below is the list of submodules available in API:

  • common : The common submodule is used by other fuc submodules such as pyvcf and pybed. It also provides many day-to-day actions used in the field of bioinformatics.
  • pybam : The pybam submodule is designed for working with sequence alignment files (SAM/BAM/CRAM). It essentially wraps the pysam package to allow fast computation and easy manipulation.
  • pybed : The pybed submodule is designed for working with BED files. It implements pybed.BedFrame which stores BED data as pandas.DataFrame via the pyranges package to allow fast computation and easy manipulation. The submodule strictly adheres to the standard BED specification.
  • pycov : The pycov submodule is designed for working with depth of coverage data from sequence alingment files (SAM/BAM/CRAM). It implements pycov.CovFrame which stores read depth data as pandas.DataFrame via the pysam package to allow fast computation and easy manipulation.
  • pyfq : The pyfq submodule is designed for working with FASTQ files. It implements pyfq.FqFrame which stores FASTQ data as pandas.DataFrame to allow fast computation and easy manipulation.
  • pymaf : The pymaf submodule is designed for working with MAF files. It implements pymaf.MafFrame which stores MAF data as pandas.DataFrame to allow fast computation and easy manipulation. The pymaf.MafFrame class also contains many useful plotting methods such as MafFrame.plot_oncoplot and MafFrame.plot_summary. The submodule strictly adheres to the standard MAF specification.
  • pysnpeff : The pysnpeff submodule is designed for parsing VCF annotation data from the SnpEff program. It should be used with pyvcf.VcfFrame.
  • pyvcf : The pyvcf submodule is designed for working with VCF files. It implements pyvcf.VcfFrame which stores VCF data as pandas.DataFrame to allow fast computation and easy manipulation. The pyvcf.VcfFrame class also contains many useful plotting methods such as VcfFrame.plot_comparison and VcfFrame.plot_tmb. The submodule strictly adheres to the standard VCF specification.
  • pyvep : The pyvep submodule is designed for parsing VCF annotation data from the Ensembl VEP program. It should be used with pyvcf.VcfFrame.

For getting help on a specific module (e.g. pyvcf):

from fuc import pyvcf
help(pyvcf)

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