Difference between revisions of "Applications/transabyss"

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==Usage==
 
==Usage==
This pipeline can be applied to assemblies generated across a wide range of k values. It first reduces the dataset into smaller sets of non-redundant contigs, and identifies splicing events including exon-skipping, novel exons, retained introns, novel introns, and alternative splicing. The Trans-ABySS algorithms are also able to estimate gene expression levels, identify potential polyadenylation sites, as well as candidate gene-fusion events [https://en.wikipedia.org/wiki/De_novo_transcriptome_assembly#Trans-ABySS]
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This pipeline can be applied to assemblies generated across a wide range of k values. It first reduces the dataset into smaller sets of non-redundant contigs and identifies splicing events including exon-skipping, novel exons, retained introns, novel introns, and alternative splicing. The Trans-ABySS algorithms are also able to estimate gene expression levels, identify potential polyadenylation sites, as well as candidate gene-fusion events [https://en.wikipedia.org/wiki/De_novo_transcriptome_assembly#Trans-ABySS]
  
 
Because of the nature of such tasks, it may be more appropriate to use [[General/Batch#High_memory_batch_job|highmem]] nodes rather than standard compute nodes.  
 
Because of the nature of such tasks, it may be more appropriate to use [[General/Batch#High_memory_batch_job|highmem]] nodes rather than standard compute nodes.  

Revision as of 13:11, 13 October 2022

Application Details

  • Description: Trans-ABySS (Assembly By Short Sequences) is a software pipeline written in Python and Perl for analyzing ABySS-assembled transcriptome contigs.
  • Version: 1.5.4
  • Modules: transabyss/1.5.5
  • Licence: BCCA (BC Cancer Agency) academic use

Usage

This pipeline can be applied to assemblies generated across a wide range of k values. It first reduces the dataset into smaller sets of non-redundant contigs and identifies splicing events including exon-skipping, novel exons, retained introns, novel introns, and alternative splicing. The Trans-ABySS algorithms are also able to estimate gene expression levels, identify potential polyadenylation sites, as well as candidate gene-fusion events [1]

Because of the nature of such tasks, it may be more appropriate to use highmem nodes rather than standard compute nodes.

Batch Submission

#!/bin/bash
#SBATCH -J transabyss
#SBATCH -N 1
#SBATCH -n 28
#SBATCH -o %N-%j.log
#SBATCH -e %N-%j.err
#SBATCH -p compute
#SBATCH --exclusive

module add transabyss/1.5.5

transabyss --pe R1.fq R2.fq --name transabyss --threads 28 -k 25 --length 200
[username@login01 ~]$ sbatch transabyss.job
Submitted batch job 289555

Further Information

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