CaptureC data analysis pipeline¶

usage: captureC.py [-h] [-j JID] (--guess_input | -f FASTQ_TSV) [-t TARGET]
                   [-l EXCLUSION_LENGTH] [-g GENOME] [-i INDEX_FILE]
                   [-s CHROM_SIZE] [-fa GENOME_FA] [-e DIGESTED_ENZYME]

optional arguments:
  -h, --help            show this help message and exit
  -j JID, --jid JID     enter a job ID, which is used to make a new directory.
                        Every output will be moved into this folder. (default:
                        captureC_yli11_2021-05-04)
  --guess_input         Let the program generate the input files for you.
                        (default: False)
  -f FASTQ_TSV, --fastq_tsv FASTQ_TSV
                        3 columns (default: None)
  -t TARGET, --target TARGET
                        9 columns (default: None)
  -l EXCLUSION_LENGTH, --exclusion_length EXCLUSION_LENGTH
                        exclusion_length (default: 1000)

Genome Info:
  -g GENOME, --genome GENOME
                        genome version: hg19, hg38, mm9, mm10. By default,
                        specifying a genome version will automatically update
                        index file, black list, chrom size and
                        effectiveGenomeSize, unless a user explicitly sets
                        those options. (default: hg19)
  -i INDEX_FILE, --index_file INDEX_FILE
                        BWA index file (default: /home/yli11/Data/Human/hg19/i
                        ndex/bowtie_1.2.2_CapC_index)
  -s CHROM_SIZE, --chrom_size CHROM_SIZE
                        chrome size (default: /home/yli11/Data/Human/hg19/anno
                        tations/hg19.chrom.sizes)
  -fa GENOME_FA, --genome_fa GENOME_FA
                        Blacklist file (default:
                        /home/yli11/Data/Human/hg19/fasta/hg19.fa)
  -e DIGESTED_ENZYME, --digested_enzyme DIGESTED_ENZYME
                        digested_fragments hg19_MboI (default: MboI)

Summary¶

Pipeline adopted from https://github.com/Hughes-Genome-Group/captureC

Only work for hg19 right now, by 5/4/2021.

This pipeline produce similar results to hicpro pipeline. However, when dealing with HBG1/HBG2 data, this pipeline appears to be better.

Input¶

fastq.tsv

Use --guess_input to automatically generate this.

Banana_R1.fastq.gz      Banana_R2.fastq.gz      Banana_lovers
Orange_R1.fastq.gz      Orange_R2.fastq.gz      Orange_lovers

Target bait bed file

at least 3 columns

chr1    123     456
chr1    454     654

Usage¶

hpcf_interactive

module load python/2.7.13

captureC.py --guess_input # to generate fastq.tsv

captureC.py -f fastq.tsv -t target.bed

Default digested enzyme is MboI, which has the same cutting site as dpnII. If you used NlaIII, then please use:

captureC.py -f fastq.tsv -t target.bed -e NlaIII -g hg19

Others¶

To make a list of dpnII cut sites:

Differential analysis or data normalization¶

The pipeline output DESEQ2.input.tsv in jid folder.

You can use it as the input for DESEQ2 pipeline. run_DESEQ2.py

Reference¶

https://github.com/Hughes-Genome-Group/captureC/releases

Notes¶

Plan to update the current pipeline to: https://github.com/Hughes-Genome-Group/CCseqBasicS

ref: https://www.nature.com/articles/s41467-019-13404-x

ref: https://www.nature.com/articles/nmeth.3664

Input OLD¶

fastq.tsv

Use --guess_input to automatically generate this.

Banana_R1.fastq.gz      Banana_R2.fastq.gz      Banana_lovers
Orange_R1.fastq.gz      Orange_R2.fastq.gz      Orange_lovers

Target bait file (MUST end with .txt)

Need absolute path to this file

Columns are: Name, chr, target_start, target_end, chr, exclusion_start, exclusion_end, 1, A.

The last two columns are almost always 1 A, which means that I don’t have a SNP defined.

Make sure there’s no empty row in this file.

HS3     11      5305797 5306271 11      5304797 5307271 1       A

code @ github.