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###Task 1

  • Implement a tool that starts with an RNA-seq BAM and produces an annotated junctions file

  • Start with an RNA-seq BAM and produce a junctions.bed file (use code from TopHat)

  • Then reimplement the functionality of this GMT in the GMS:


  • Test case to be reproduced (from within a TGI system where the GMS is installed):
git clone
cd regtools/tests
gunzip *.gz
rm -fr /tmp/junction_summary/
mkdir /tmp/junction_summary/
gmt transcriptome splice-junction-summary --output-directory='/tmp/junction_summary/' --observed-junctions-bed12-file='junctions.chr22.bed' --reference-fasta-file='chr22.fa' --annotation-gtf-file='genes_chr22.gtf' --annotation-name='Ensembl'
cd /tmp/junction_summary/
rm -fr SpliceJunctionSummary.R.stderr summary SpliceJunctionSummary.R.stdout Ensembl.Junction.TranscriptExpression.top1percent.tsv Ensembl.Junction.GeneExpression.top1percent.tsv

###MGI datasets for testing regtools analysis Requirements: WGS (or exome maybe) and RNA-seq on the same sample. Ideally a tumor/normal pair where we have RNA-seq for a matched adjacent normal.

#Look for a model/build to use genome model rna-seq list --filter --show id,processing_profile,,last_complete_build.merged_alignment_result.bam_path --noheaders --style tsv | grep -v NULL

HCC1395 (no matched adjacent normal, but there is a matched blood normal with RNA-seq) Normal WGS model: 2891325882 Tumor WGS model: 2891325873 Tumor RNA-seq model: 060145d385274d258569a9fc013e4ada

genome model list --filter 'id in [2891325882,2891325873,060145d385274d258569a9fc013e4ada]' --show last_complete_build.merged_alignment_result.bam_path --noheaders | perl -ne 'print "$_"'
  • ALL1 (no matched adjacent normal, but we can use the 'healthy' normals for comparison)
  • AML31 (no matched adjacent normal, but we can compare primary and relapse)
  • HCC30 (has matched adjacent normal RNA-seq)
  • LUC1-20 (has matched adjacent normal RNA-seq)