converttranscriptfile
converttranscriptfile converts transcript files between formats to enable interoperability of transcriptomic data across bioinformatics tools and pipelines.
Key Features:
- Format Conversion: Transforms transcript data files among multiple formats to satisfy input requirements of downstream analysis tools.
- Galaxy Integration: Executes conversions within the Galaxy@Pasteur instance using Galaxy's execution engine on the Institut Pasteur compute cluster.
- API and Bioblend Communication: Invokes Galaxy workflows and jobs through the Galaxy API or the Bioblend library for programmatic control.
Scientific Applications:
- Genomics: Provides format compatibility for transcriptomic workflows for gene expression analysis and transcriptome processing tools.
- Metagenomics: Ensures transcript file formats are compatible with metagenomic analysis pipelines and tools used for community transcript profiling.
- Phylogenetics: Supplies appropriately formatted transcript data for phylogenetic analyses that require specific input file types.
Methodology:
Performs format conversions by submitting jobs to Galaxy's execution engine on the Institut Pasteur cluster, communicating with Galaxy via its API or the Bioblend library.
Topics
Collections
Details
- Maturity:
- Mature
- Tool Type:
- web application
- Operating Systems:
- Linux, Windows, Mac
- Added:
- 12/19/2016
- Last Updated:
- 6/16/2020
Operations
Data Inputs & Outputs
Formatting
Publications
Afgan E, Baker D, van den Beek M, Blankenberg D, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Eberhard C, Grüning B, Guerler A, Hillman-Jackson J, Von Kuster G, Rasche E, Soranzo N, Turaga N, Taylor J, Nekrutenko A, Goecks J. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Research. 2016;44(W1):W3-W10. doi:10.1093/nar/gkw343. PMID:27137889. PMCID:PMC4987906.
Mareuil F, Doppelt-Azeroual O, Ménager H. A public Galaxy platform at Pasteur used as an execution engine for web services. Unknown Journal. 2017. doi:10.7490/f1000research.1114334.1.