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

Authors :
Lorenzo Arcioni
Manuel Arcieri
Jessica Di Martino
Franco Liberati
Paolo Bottoni
Tiziana Castrignanò
Source :
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-14 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

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. 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. 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.

Details

Language :
English
ISSN :
14712105
Volume :
25
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.f24ef9c0bacc4e249d8e5cd4f4cd3ba9
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-024-05887-3