pipeline/README.md

In daewoooo/SaaRclust: R Package to cluster long sequencing reads into chromosomes to facilitate de novo genome assembly.

SaaRclust pipeline

Ongoing work

Clustering PacBio reads into their original chromosomes and directionalities using Strand-seq data - summarized in a Snakemake pipeline.

How to use it

1. Download the pipeline Download the pipeline directory including Snakefile, the perl script processMinimaptab.pl, and a subdirectory "utils" containing the following Rscripts:

   • "install_SaaRclust.R" : Rscript for installing SaaRclust R package
   • "SaaRclust_hardclust_pipeline.R" : Rscript for running hard clustering
   • "SaaRclust_softclust_pipeline.R" : Rscript for running soft clustering

2. Install required software:

   • Install minimap for aligning Strand-seq reads to PacBio reads.

3. Prepare the input data

   • Move to the directory containg the Snakefile
   • Create a directory with name "raw_reads"
   • Copy or (soft-link) the fastq file containing the raw Strand-seq reads in "raw_reads" directory
   • Create a directory "chunks" in "raw_reads" directory
   • Copy or (soft-link) the fasta formatted files containing PacBio reads in "raw_reads/chunks" directory. The names of files should have the format "{common_prefix}chunk{chunk_id}". Note that the files do not have to have the ".fasta" or ".fa" extension

4. Set up the parameters of the snakemake pipeline

   • Open Snakefile and set the parameters of minimap and SaaRclust at the beginning of the file (such as Mosaicatcher) and to the R scripts.

5. Run Snakemake

   • Run snakemake to compute all tasks locally

The output files

Snakemake creates the following output files and directories:

1. Minimap alignment results in directory "aligns"

   • The alignment files with names {PacBio_read_file_name}.maf.gz
   • Minimap output summaries with names {PacBio_read_file_name}.maf.gz.log

2. SaaRclust results

   • SaaRclust summaries in directory "log"
   • SaaRclust hard and soft clustering (in different chunks) results saved as RData objects in directory "SaaRclust_results_{sample_name}/Clusters"
   • Some supplementary data like blacklisted alignments and some data quality measures

daewoooo/SaaRclust documentation built on May 28, 2019, 7:50 p.m.