Difference between revisions of "Applications/transabyss"
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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] | 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 [[ | + | Because of the nature of such tasks, it may be more appropriate to use [[FurtherTopics/Advanced_Batch_Jobs#highmem|highmem]] nodes rather than standard compute nodes. |
=== Batch Submission === | === Batch Submission === | ||
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* http://www.bcgsc.ca/platform/bioinfo/software/trans-abyss | * http://www.bcgsc.ca/platform/bioinfo/software/trans-abyss | ||
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* https://en.wikipedia.org/wiki/De_novo_transcriptome_assembly#Trans-ABySS | * https://en.wikipedia.org/wiki/De_novo_transcriptome_assembly#Trans-ABySS | ||
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Latest revision as of 10:12, 22 November 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