Patidarr Ngs Pipeline Save


Introduction

This is the implementation of KhanLab NGS Pipeline using Snakemake.

Installation

The easiest way to get this pipeline is to clone the repository.

git clone https://github.com/patidarr/ngs_pipeline.git

This pipeline is available on NIH biowulf cluster, contact me if you would like to do a test run. The data from this pipeline could directly be ported in OncoGenomics-DB, an application created to visualize NGS data available to NIH users.

Requirements

mutt
gnu parallel
SLURM or PBS for resource management
Bioinformatics Tools Listed in config files

Following R Packages

• OmicCircos
• RColorBrewer
• Rsubread
• edgeR
• limma
• optparse
• pracma
• sequenza
• stats
• stringr
• deconstructSigs

Conventions

• Sample names cannot have "/" or "." in them
• Fastq files end in ".fastq.gz"
• Fastq files are stored in DATA_DIR (Set as Environment Variable)

DNASeq:

• QC
• BWA, Novoalign
• Broad Standard Practices on bwa bam
• Haplotype Caller, Platupys, Bam2MPG, MuTect, Strelka
• snpEff, Annovar, SIFT, pph2, Custom Annotation
• Coverage Plot, Circos Plot, Hotspot Coverage Box Plot
• Create input format for oncogenomics database (Patient Level)
• Make Actionable Classification for Germline and Somatic Mutations
• Copy number based on the simple T/N LogRatio (N cov >=30), Corrected for Total # Reads
• Copy number, tumor purity using sequenza
• LRR adjusted to center
• Contamination using conpair
• HLA Typing
  • HLAminer v1.3.1
  • seq2HLA
• Neoantigen Prediction
  • pVAC-Seq methods: NNalign,NetMHC,NetMHCIIpan,NetMHCcons,NetMHCpan,PickPocket,SMM,SMMPMBEC,SMMalign
    epitope length: 8,9,10,11

RNASeq:

• QC
• Tophat, STAR
• Broad Standard Practices on STAR bam
• fusion-catcher, tophat-fusion, deFuse
• Cufflinks (ENS and UCSC)
• Rsubread TPM (ENS, UCSC), Gene, Transcript and Exon Level
• In-house Exon Expression (ENS and UCSC)
• Haplotype Caller
• snpEff, Annovar, SIFT, pph2, Custom Annotation
• Actionable Fusion classification

Patient:

• Genotyping On Patient. 1000g sites are evaluated for every library and then compared (all vs all) If two libraries come from a patient the match should be pretty good >80%
• Still to develop: If the match is below a certain threshold, break the pipeline for patient.

Rulegraph

DAG for example Sample