Evidence map›Paper›PMID 39169276›Full record

ArticleBMC bioinformatics2024

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

Lorenzo Arcioni, Manuel Arcieri, Jessica Di Martino, Franco Liberati, Paolo Bottoni, Tiziana Castrignanò

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. MultipleFood and waterborne parasitology · 2025
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lorenzo ArcioniDepartment of Computer Science, Sapienza University of Rome, Viale Regina Elena 295, 00166, Rome, Italy.
Manuel ArcieriDepartment of Health Technology, Technical University of Denmark, Anker Engelunds Vej 101, 2800, Kongens Lyngby, Denmark.
Jessica Di MartinoDepartment of Ecological and Biological Sciences, University of Tuscia, Viale dell'Università s.n.c., 01100, Viterbo, Italy.
Franco LiberatiDepartment of Computer Science, Sapienza University of Rome, Viale Regina Elena 295, 00166, Rome, Italy.
Paolo BottoniDepartment of Computer Science, Sapienza University of Rome, Viale Regina Elena 295, 00166, Rome, Italy. bottoni@di.uniroma1.it.
Tiziana CastrignanòDepartment of Ecological and Biological Sciences, University of Tuscia, Viale dell'Università s.n.c., 01100, Viterbo, Italy. tiziana.castrignano@unitus.it.

Funding

Ministry of University and Research, Italy B87G22000450001
6 · The paper itself

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.

Indexed as

Molecular Sequence AnnotationSoftwareTranscriptomeComputational BiologyDatabases, GeneticGene Expression ProfilingBioinformaticsData-parallelism algorithmHigh performance computingTranscript annotation

Identifiers

PMID39169276
PMCPMC11340092

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LicenceCC BY-NC-ND
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None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.