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HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.

Authors :
Arcioni L
Arcieri M
Martino JD
Liberati F
Bottoni P
Castrignanò T
Source :
BMC bioinformatics [BMC Bioinformatics] 2024 Aug 21; Vol. 25 (1), pp. 272. Date of Electronic Publication: 2024 Aug 21.
Publication Year :
2024

Abstract

Background: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure.<br />Results: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software.<br />Conclusions: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
1471-2105
Volume :
25
Issue :
1
Database :
MEDLINE
Journal :
BMC bioinformatics
Publication Type :
Academic Journal
Accession number :
39169276
Full Text :
https://doi.org/10.1186/s12859-024-05887-3