Evidence map›Paper›PMID 39969909›Full record

ArticleBioinformatics (Oxford, England)2025

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

Máté Balajti, Rohan Kandhari, Boris Jurič, Mihaela Zavolan, Alexander Kanitz

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Máté BalajtiBiozentrum, University of Basel, Basel 4056, Switzerland.ORCID 0009-0000-3932-3964
Rohan KandhariBiozentrum, University of Basel, Basel 4056, Switzerland.
Boris JuričInstitute of Computer Science, Masaryk University, Brno 60200, Czech Republic.
Mihaela ZavolanBiozentrum, University of Basel, Basel 4056, Switzerland.ORCID 0000-0002-8832-2041
Alexander KanitzBiozentrum, University of Basel, Basel 4056, Switzerland.ORCID 0000-0002-3468-0652

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

summaryThe Sequencing Read Archive is one of the largest and fastest-growing repositories of sequencing data, containing tens of petabytes of sequenced reads. Its data is used by a wide scientific community, often beyond the primary study that generated them. Such analyses rely on accurate metadata concerning the type of experiment and library, as well as the organism from which the sequenced reads were derived. These metadata are typically entered manually by contributors in an error-prone process, and are frequently incomplete. In addition, easy-to-use computational tools that verify the consistency and completeness of metadata describing the libraries to facilitate data reuse, are largely unavailable. Here, we introduce HTSinfer, a Python-based tool to infer metadata directly and solely from bulk RNA-sequencing data generated on Illumina platforms. HTSinfer leverages genome sequence information and diagnostic genes to rapidly and accurately infer the library source and library type, as well as the relative read orientation, 3' adapter sequence and read length statistics. HTSinfer is written in a modular manner, published under a permissible free and open-source license and encourages contributions by the community, enabling easy addition of new functionalities, e.g. for the inference of additional metrics, or the support of different experiment types or sequencing platforms. AVAILABILITY AND IMPLEMENTATION: HTSinfer is released under the Apache License 2.0. Latest code is available via GitHub at https://github.com/zavolanlab/htsinfer, while releases are published on Bioconda. A snapshot of the HTSinfer version described in this article was deposited at Zenodo at 10.5281/zenodo.13985958.

Indexed as

High-Throughput Nucleotide SequencingMetadataRNA-SeqSequence Analysis, RNASoftwareComputational BiologyGene Library

Identifiers

PMID39969909
PMCPMC11889452

What OpenQuestion holds

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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.