ArticleBMC bioinformatics2024
HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MultipleFood and waterborne parasitology · 2025Article
- Comparative transcriptomics reveals different profiles between diflubenzuron-resistant and -susceptible phenotypes of the mosquito Culex pipiens.Pest management science · 2025Article
- HPC-T-Assembly: a pipeline for de novo transcriptome assembly of large multi-specie datasets.BMC bioinformatics · 2025Article
- De novo transcriptome assembly of the Mediterranean sea-rock pool mosquitoes Aedes mariae and Aedes zammitii.Scientific data · 2025Article
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6 authors.
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Abstract
backgroundThe 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.
resultsTo 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.
conclusionsHPC-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.
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