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Introduction

DOPseq_analyzer is a set of tools for processing high-throughput sequencing data generated from isolated (flow sorted or microdissected) chromosomes. Active development is now moved to https://github.com/lca-imcb/dopseq - a re-implementation based on Conda and Snakemake.

Currently, three pipelines are implemented:

  1. Chromosomal region prediction pipeline (dopseq_pipeline) includes read trimming, alignment to reference genome, contamination filtering, region calling and statistics calculation.
  2. Variant calling and annotation pipeline for validated chromosome-specific regions (variation_pipeline).
  3. Analysis of anole microchromosomes (anolis/pipeline/anolis_dopseq_pipe.py) includes steps similar to dopseq_pipeline. It is optimized for reference genomes consisting of scaffolds and has a possibility to handle WGA libraries. This pipeline is not included in the pipeline installation and can be called only directly. Maintained by ilyakichigin.

Installation

Dependencies for dopseq_pipeline:

  1. bowtie2 (tested on v.2.1.0, 2.2.4) or bwa (tested on v.0.7.12)

  2. bedtools (tested on v.2.17.0 and v.2.24.0)

  3. DNAcopy R (Bioconductor) package

Dependencies for variation_pipeline:

  1. GATK 4 (tested on beta.1)

  2. snpEff (tested on v.3.3.0 and v.4.3t)

Installation:

git clone https://github.com/ilyakichigin/DOPseq_analyzer.git
cd DOPseq_analyzer
pip install --user .

Usage

Pipeline for chromosome region identification can be called with the command:

dopseq_pipeline [-c|-d|-s] dopseq_makefile.yaml

Makefile example can be found at examples/dopseq_makefile.yaml in this repository or copied to your working directory by running dopseq_pipeline -c my_makefile.yaml. Use this file to specify your input data and parameters.

Use dopseq_pipeline -s dopseq_makefile.yaml > genome.sizes to generate tab-separated file listing chromosomes and their sizes for the reference genome specified in the makefile.

Dry run -d option provides command listing and checks if all the input files are present. After that, pipeline can be run with dopseq_pipeline my_makefile.yaml

Briefly, this pipeline trims and aligns reads to the reference genome (and optonally contaminant genome, human being most obvious for mammalian chromosome samples), filters the alignment, and classifies selected chromosomes of the reference genome based on mean distances between mapped read positions. Regions with lower means can be further interpreted as present on the isolated chromosomes. Note that these regions cannot be used 'as is' and require manual inspection and correction. Pipeline steps and output files are described in the example makefile.

Pipeline for variant calling and annotation can be called similarly:

variation_pipeline [-d|-c] variation_makefile.yaml

Currently, this pipeline runs GATK HaplotypeCaller and snpEff annotation with default settings for a given set of genome regions (presumably present on the chromosome of interest). It also creates a file with variation summary, including per region and per gene statistics.