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rnaseqlib's Introduction

rnaseqlib

A collection of Python modules & Rscripts for analysis of RNA-seq data for the following publication:

Mutational Landscape of the Transcriptome Offers Putative Targets for Immunotherapy of Myeloproliferative Neoplasms.

Fiorella Schischlik, Roland Jäger, Felix Rosebrock, Eva Hug, Michael Schuster, Raimund Holly, Elisabeth Fuchs, Jelena D Milosevic Feenstra, Edith Bogner, Bettina Gisslinger, Martin Schalling, Elisa Rumi, Daniela Pietra, Gottfried Fischer, Ingrid Faé, Loan Vulliard, Jörg Menche, Torsten Haferlach, Manja Meggendorfer, Anna Stengel, Christoph Bock, Mario Cazzola, Heinz Gisslinger, Robert Kralovics. May 2019, Blood 134(2) DOI: 10.1182/blood.2019000519

Software requirements

Fusion detection

Variant calling

Aberrant splicing

Python modules (not specific for any workflow)

Environment variables used

  • $TMPDIR (path to temporary directory)
  • $NGS_GATK (path to gatk executables)
  • $NGS_PICARD (path to picard executables)

Implemented workflows

License

rnaseqlib is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

rnaseqlib is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

rnaseqlib's People

Contributors

sp00nman avatar

Stargazers

0x1orz avatar Dan Liu avatar Michael K. Schuster avatar  avatar

Watchers

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rnaseqlib's Issues

Additional filtering

SNiPR suggests the following filtering steps:
Call/filter Variants (see EXAMPLES.sh for more information):
Step 1. Initial variant calling with GATK UnifiedGenotyper: options of -stand_call_conf 0 -stand_emit_conf 0
Step 2. Convert VCF format to our custom variant format and filter variants with low quality: ConvertVCF.sh in.vcf out.txt MINQUAL
Step 3. Remove mismatches in first 6 bp of reads: perl filter_mismatch_first6bp.pl
Step 4. Use bedtools to remove sites in repetitive regions based on RepeatMasker annotation

Further filtering:
Step 1. Filter intronic candidates that are within 4 bp of splicing junctions: perl filter_intron_near_splicejuncts.pl
Step 2. Filter candidates in homopolymer runs: perl filter_homopolymer_nucleotides.pl
Step 3. Use BLAT to ensure unique mapping: perl BLAT_candidates.pl
Step 4. Use bedtools to separate out candidates that are known RNA editing sites

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