Evidence map›Paper›PMID 39703424›Full record

ArticleNAR genomics and bioinformatics2024

TSS-Captur: a user-friendly pipeline for characterizing unclassified RNA transcripts.

Mathias Witte Paz, Thomas Vogel, Kay Nieselt

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

3 authors.

Mathias Witte PazInstitute for Bioinformatics and Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen 72076, Germany.ORCID https://orcid.org/0000-0001-8113-4139
Thomas VogelInstitute for Bioinformatics and Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen 72076, Germany.
Kay NieseltInstitute for Bioinformatics and Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen 72076, Germany.ORCID https://orcid.org/0000-0002-1283-7065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RNA-seq and its 5'-enrichment methods for prokaryotes have enabled the precise identification of transcription start sites (TSSs), improving gene expression analysis. Computational methods are applied to these data to identify TSSs and classify them based on proximal annotated genes. While some TSSs cannot be classified at all (orphan TSSs), other TSSs are found on the reverse strand of known genes (antisense TSSs) but are not associated with the direct transcription of any known gene. Here, we introduce TSS-Captur, a novel pipeline, which uses computational approaches to characterize genomic regions starting from experimentally confirmed but unclassified TSSs. By analyzing TSS data, TSS-Captur characterizes unclassified signals, complementing prokaryotic genome annotation tools. TSS-Captur categorizes extracted transcripts as either messenger RNA for genes with coding potential or non-coding RNA (ncRNA) for non-translated genes. Additionally, it predicts the transcription termination site for each putative transcript. For ncRNA genes, the secondary structure is computed. Moreover, all putative promoter regions are analyzed to identify enriched motifs. An interactive report allows seamless data exploration. We validated TSS-Captur with a

Identifiers

PMID39703424
PMCPMC11655288

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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.