README

Introduction

The main goal of the fuc package (pronounced "eff-you-see") is to wrap some of the most frequently used commands in the field of bioinformatics into one place.

The package is written in Python, and supports both command line interface (CLI) and application programming interface (API) whose documentations are available at the Read the Docs.

Currently, fuc can be used to analyze, summarize, visualize, and manipulate the following file formats:

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

Additionally, fuc can be used to parse output data from the following programs:

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

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

Author: Seung-been "Steven" Lee

Email: [email protected]

License: MIT License

Citation

If you use fuc in a published analysis, please report the program version and cite the following article:

Lee et al., 2022. ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation. PLOS ONE.

Support fuc

If you find my work useful, please consider becoming a sponsor.

Installation

The following packages are required to run fuc:

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

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

$ conda install -c bioconda fuc

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

$ pip install fuc

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

$ 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 the fuc package's CLI and API, please refer to the Read the Docs.

For getting help on the fuc CLI:

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

positional arguments:
  COMMAND
    bam-aldepth  Compute allelic depth from a BAM file.
    bam-depth    Compute per-base read depth from BAM files.
    bam-head     Print the header of a BAM file.
    bam-index    Index a BAM file.
    bam-rename   Rename the sample in a BAM file.
    bam-slice    Slice a BAM file.
    bed-intxn    Find the intersection of BED files.
    bed-sum      Summarize a BED file.
    cov-concat   Concatenate depth of coverage files.
    cov-rename   Rename the samples in a depth of coverage file.
    fa-filter    Filter sequence records in a FASTA file.
    fq-count     Count sequence reads in FASTQ files.
    fq-sum       Summarize a FASTQ file.
    fuc-bgzip    Write a BGZF compressed file.
    fuc-compf    Compare the contents of two files.
    fuc-demux    Parse the Reports directory from bcl2fastq.
    fuc-exist    Check whether certain files exist.
    fuc-find     Retrieve absolute paths of files whose name matches a
                 specified pattern, optionally recursively.
    fuc-undetm   Compute top unknown barcodes using undertermined FASTQ from
                 bcl2fastq.
    maf-maf2vcf  Convert a MAF file to a VCF file.
    maf-oncoplt  Create an oncoplot with a MAF file.
    maf-sumplt   Create a summary plot with a MAF file.
    maf-vcf2maf  Convert a VCF file to a MAF file.
    ngs-bam2fq   Pipeline for converting BAM files to FASTQ files.
    ngs-fq2bam   Pipeline for converting FASTQ files to analysis-ready BAM
                 files.
    ngs-hc       Pipeline for germline short variant discovery.
    ngs-m2       Pipeline for somatic short variant discovery.
    ngs-pon      Pipeline for constructing a panel of normals (PoN).
    ngs-quant    Pipeline for running RNAseq quantification from FASTQ files
                 with Kallisto.
    ngs-trim     Pipeline for trimming adapters from FASTQ files.
    tabix-index  Index a GFF/BED/SAM/VCF file with Tabix.
    tabix-slice  Slice a GFF/BED/SAM/VCF file with Tabix.
    tbl-merge    Merge two table files.
    tbl-sum      Summarize a table file.
    vcf-call     Call SNVs and indels from BAM files.
    vcf-filter   Filter a VCF file.
    vcf-index    Index a VCF file.
    vcf-merge    Merge two or more VCF files.
    vcf-rename   Rename the samples in a VCF file.
    vcf-slice    Slice a VCF file for specified regions.
    vcf-split    Split a VCF file by individual.
    vcf-vcf2bed  Convert a VCF file to a BED file.
    vcf-vep      Filter a VCF file by annotations from Ensembl 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 the fuc 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. If you are mainly interested in working with depth of coverage data, please check out the pycov submodule which is specifically designed for the task.
• 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.
• pychip : The pychip submodule is designed for working with annotation or manifest files from the Axiom (Thermo Fisher Scientific) and Infinium (Illumina) array platforms.
• 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. The pycov.CovFrame class also contains many useful plotting methods such as CovFrame.plot_region and CovFrame.plot_uniformity.
• 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.
• pygff : The pygff submodule is designed for working with GFF/GTF files. It implements pygff.GffFrame which stores GFF/GTF data as pandas.DataFrame to allow fast computation and easy manipulation. The submodule strictly adheres to the standard GFF specification.
• pykallisto : The pykallisto submodule is designed for working with RNAseq quantification data from Kallisto. It implements pykallisto.KallistoFrame which stores Kallisto's output data as pandas.DataFrame to allow fast computation and easy manipulation. The pykallisto.KallistoFrame class also contains many useful plotting methods such as KallistoFrame.plot_differential_abundance.
• 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 submodule (e.g. pyvcf):

>>> from fuc import pyvcf
>>> help(pyvcf)

In Jupyter Notebook and Lab, you can see the documentation for a python function by hitting SHIFT + TAB. Hit it twice to expand the view.

CLI examples

SAM/BAM/CRAM

To print the header of a SAM file:

$ fuc bam-head in.sam

To index a CRAM file:

$ fuc bam-index in.cram

To rename the samples in a SAM file:

$ fuc bam-rename in.sam NA12878 > out.sam

To slice a BAM file:

$ fuc bam-slice in.bam chr1:100-200 > out.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.gz

TABLE

To merge two tab-delimited files:

$ fuc tbl-merge left.tsv right.tsv > merged.tsv

VCF

To merge VCF files:

$ fuc vcf-merge 1.vcf 2.vcf 3.vcf > merged.vcf

To filter a VCF file annotated by Ensembl VEP:

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

API examples

BAM

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('chr12:21161194-21239796')

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)

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_col='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, group_col='Tissue')
>>> vf.plot_hist_format('#DP', ax=ax3, af=af, group_col='Tissue')
>>> vf.plot_regplot(normal, tumor, ax=ax4)
>>> plt.tight_layout()

MAF

To create an oncoplot with a MAF file:

>>> from fuc import common, pymaf
>>> common.load_dataset('tcga-laml')
>>> maf_file = '~/fuc-data/tcga-laml/tcga_laml.maf.gz'
>>> mf = pymaf.MafFrame.from_file(maf_file)
>>> mf.plot_oncoplot()

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

To create a summary figure for a MAF file:

>>> from fuc import common, pymaf
>>> common.load_dataset('tcga-laml')
>>> maf_file = '~/fuc-data/tcga-laml/tcga_laml.maf.gz'
>>> mf = pymaf.MafFrame.from_file(maf_file)
>>> mf.plot_summary()