fuc 0.7.0

Frequently used commands in bioinformatics

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:

• Variant Call Format (VCF)

• 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

Email: [e-mail]

License: MIT License

CLI Examples

To find intersection between BED files:

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

To merge two tab-delimited files:

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

To check whether a file exists in the operating system:

$ fuc fucexist example.txt

To find all VCF files within the current directory recursively:

$ fuc fucfind . vcf

To count sequence reads in a FASTQ file:

$ fuc qfcount example.fastq

To merge VCF files:

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

API Examples

To filter a VCF file based on a BED file:

from fuc import pyvcf
vf = pyvcf.read_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.read_file('with_indels.vcf')
filtered_vf = vf.filter_indel()
filtered_vf.to_file('no_indels.vcf')

Required Packages

The following packages are required to run fuc:

biopython
numpy
pandas
pyranges

Getting Started

There are various ways you can install fuc. The easiest one would be to use pip:

$ pip install fuc

Above will automatically download and install all the dependencies as well. Alternatively, you can clone the GitHub repository and then install fuc this way:

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

Above will also allow you to install a development version that’s not available in PyPI.

For getting help on CLI:

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

positional arguments:
  COMMAND        name of the command
    bfintxn      [BED] find intersection of two or more BED files
    bfsum        [BED] summarize a BED file
    dfmerge      [TABLE] merge two table files
    dfsum        [TABLE] summarize a table file
    fuccompf     [FUC] compare contents of two files
    fucdemux     [FUC] parse Reports directory from bcl2fastq or bcl2fastq2
    fucexist     [FUC] check whether files/dirs exist
    fucfind      [FUC] find files with certain extension recursively
    qfcount      [FASTQ] count sequence reads in FASTQ files
    qfsum        [FASTQ] summarize a FASTQ file
    vfmerge      [VCF] merge two or more VCF files
    vfslice      [VCF] slice a VCF file

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. vfmerge):

$ fuc vfmerge -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.

• 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.

• pyfq : The pyfq submodule is designed for working with FASTQ files (both zipped and unzipped). It implements pyfq.FqFrame which stores FASTQ data as pandas.DataFrame to allow fast computation and easy manipulation.

• pysnpeff : The pysnpeff submodule is designed for parsing VCF annotation data from the SnpEff program. It is designed to be used with pyvcf.VcfFrame.

• pyvcf : The pyvcf submodule is designed for working with Variant Call Format (VCF) files (both zipped and unzipped). It implements pyvcf.VcfFrame which stores VCF data as pandas.DataFrame to allow fast computation and easy manipulation. The submodule strictly adheres to the standard VCF specification.

• pyvep : The pyvep submodule is designed for parsing VCF annotation data from the Ensembl Variant Effect Predictor (VEP). It is designed to be used with pyvcf.VcfFrame.

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

from fuc import pyvcf
help(pyvcf)