Metadata-Version: 1.1
Name: TargQC
Version: 1.5.2
Summary: Genome capture target coverage evaluation tool
Home-page: https://github.com/vladsaveliev/TargQC
Author: Vlad Saveliev and Alla Mikheenko
Author-email: vladislav.sav@gmail.com
License: GPLv3
Download-URL: https://github.com/vladsaveliev/TargQC/releases
Description: [![Anaconda-Server Badge](https://anaconda.org/vladsaveliev/targqc/badges/installer/conda.svg)](https://conda.anaconda.org/vladsaveliev)
        [![Build Status](https://travis-ci.org/vladsaveliev/TargQC.svg?branch=master)](https://travis-ci.org/vladsaveliev/TargQC)
        
        # TargQC - target capture coverage QC tool
        
        ## Input
        - BAM file(s) (or FastQ files).
        - BED file (optional).
        
        ## Output
        - `summary.html` – sample-level coverage statistics and plots.
        - `summary.tsv` – sample-level coverage, parsable version
        - `regions.txt` – region-level coverage statistics.
        
        ## Installation
        ### From bioconda
        ```
        conda install -c bioconda targqc
        ```
        
        ### From PyPI
        ```
        pip install targqc
        ```
        
        ### From source
        ```
        git clone --recursive https://github.com/vladsaveliev/TargQC
        cd TargQC
        virtualenv venv_targqc && source venv_targqc/bin/activate  # optional, but recommended if you are not an admin
        pip install --upgrade pip setuptools
        pip install -r requirements.txt
        python setup.py install
        ```
        
        ## Usage
        ```
        targqc *.bam --bed target.bed -g hg19 -o targqc_results
        ```
        The results will be written to `targqc_results` folder.
        
        The BED file may be omitted. In this case statistics reported will be based of off the whole genome.
        
        The accepted values for `-g` are `hg19`, `hg38`, or a full path to any indexed reference fasta file:
        ```
        targqc *.bam --bed target.bed -g /path/to/genomes/some_genome.fa -o targqc_results
        ```
        When running from BAMs, only the `.fai` index is used, and the fasta file itself can be non-existent.
        
        Instead of the BAM files, input FastQ are also allowed. The reads will be aligned by BWA to the reference 
        genome specified by `--bwa-prefix` (unless `-g` is already a fasta path bwa-indexed).
        ```
        targqc *.fastq --bed target.bed -g hg19 -o targqc_results --bwa-prefix /path/to/ref.bwa
        ```
        Option `--downsample-to <N>` (default value `5e5`) specifies the number of 
        read pairs will be randomly selected from each input set. This feature allows to quickly estimate approximate 
        coverage quality before full alignment. To turn downsampling off and align all reads, set `--downsample-to off`.
        
        
        ## Parallel running
        ### Threads
        Run using 3 threads:
        ```
        targqc *.bam --bed target.bed -g hg19 -o targqc_results -t 3
        ```
        ### Cluster
        Run using 3 jobs, using SGE scheduler, and queue "queue":
        ```
        targqc *.bam --bed target.bed -g hg19 -o targqc_results -t 3 -s sge -q batch.q -r pename=smp
        ```
        If the number of samples is higher than the requested number of jobs, the processes within job will be additionally parallelized using threads, so the full number of occupied cores will equal the number of requested threads (-t)
        
        Other supported schedulers: Platform LSF ("lsf"), Sun Grid Engine ("sge"), Torque ("torque"), SLURM ("slurm") (see details at https://github.com/roryk/ipython-cluster-helper)
        
        
        # BED file annotation
        
        The `bed_annotation` package provides tools for annotation of BED file regions with gene symbols, based on Ensembl data.
        
        ### Usage
        ```
        annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed
        ``` 
        
        Script checks each region against the Ensembl genomic features database, and writes a BED file in a standardized format with a gene symbol, strand and exon rank in 4-6th columns:
        
        `INPUT.bed`:
        ```
        chr1    69090   70008
        chr1    367658  368597
        ```
        
        `OUTPUT.bed`:
        ```
        chr1    69090   70008   OR4F5   1       +
        chr1    367658  368597  OR4F29  1       +
        ```
        
        #### Transcripts order
        
        The piority for choosing transcripts for annotation is the following:
        - Overlap % with transcript
        - Overlap % with CDS
        - Overlap % with exons
        - Biotype (`protein_coding` > others > `*RNA` > `*_decay` > `sense_*` > `antisense` > `translated_*` > `transcribed_*`)
        - TSL (1 > NA > others > 2 > 3 > 4 > 5)
        - Presence of a HUGO gene symbol
        - Is cancer canonical
        - Transcript size
        
        #### Extended annotation
        
        Use `--extended` option to report extra columns with details on features, biotype, overlapping transcripts and overlap sizes:
        ```
        annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed --extended
        ```
        
        `OUTPUT.bed`:
        ```
        ## Tx_overlap_%: part of region overlapping with transcripts
        ## Exon_overlaps_%: part of region overlapping with exons
        ## CDS_overlaps_%: part of region overlapping with protein coding regions
        #Chrom  Start   End     Gene    Exon    Strand  Feature Biotype Ensembl_ID      TSL     HUGO    Tx_overlap_%    Exon_overlaps_% CDS_overlaps_%
        chr1    69090   70008   OR4F5   1       +       capture protein_coding  ENST00000335137 NA      OR4F5   100.0   100.0   99.7
        chr1    367658  368597  OR4F29  1       +       capture protein_coding  ENST00000426406 NA      OR4F29  100.0   100.0   99.7
        ```
        
        #### Ambuguous annotations
        
        Regions may overlap mltiple genes. The `--ambiguities` controls how the script resolves such ambiguities
        - `--ambiguities all` -- report all reliable overlaps (in order in the "priority" section, see above)
        - `--ambiguities all_ask` -- stop execution and ask user which annotation to pick
        - `--ambiguities best_all` (default) -- find the best overlap, and if there are several equally good, report all (in terms of the "priority" above)
        - `--ambiguities best_ask` -- find the best overlap, and if there are several equally good, ask user
        - `--ambiguities best_one` -- find the best overlap, and if there are several equally good, report any of them
        
        Note that the first 4 options might output multiple lines per region, e.g.:
        ```
        annotate_bed.py INPUT.bed -g hg19 -o OUTPUT.bed --extended --ambiguities best_all
        ```
        `OUTPUT.bed`:
        ```
        ## Tx_overlap_%: part of region overlapping with transcripts
        ## Exon_overlaps_%: part of region overlapping with exons
        ## CDS_overlaps_%: part of region overlapping with protein coding regions
        #Chrom  Start   End     Gene    Exon    Strand  Feature Biotype Ensembl_ID      TSL     HUGO    Tx_overlap_%    Exon_overlaps_% CDS_overlaps_%
        chr1    69090   70008   OR4F5   1       +       capture protein_coding  ENST00000335137 NA      OR4F5   100.0   100.0   100.0
        chr1    367658  368597  OR4F29  1       +       capture protein_coding  ENST00000426406 NA      OR4F29  100.0   100.0   100.0
        chr1    367658  368597  OR4F29  1       +       capture protein_coding  ENST00000412321 NA      OR4F29  100.0   100.0   100.0
        ```
        
        # Venn diagrams for BED files
        Build a web-page with size-proportional Venn diagrams for an unlimited set of BED files:
        ```
        bed_venn.py *.bed -o res_dir
        ```
        
Keywords: bioinformatics
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: JavaScript
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
