Evidence map›Paper›PMID 36401177›Full record

ArticleBMC bioinformatics2022

srnaMapper: an optimal mapping tool for sRNA-Seq reads.

Matthias Zytnicki, Christine Gaspin

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Matthias ZytnickiUnité de Mathématiques et Informatique Appliquées, INRAE, Castanet-Tolosan, France. matthias.zytnicki@inrae.fr.
Christine GaspinUnité de Mathématiques et Informatique Appliquées, INRAE, Castanet-Tolosan, France.
Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement · FRMathématiques et Informatique Appliquées

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSequencing is the key method to study the impact of short RNAs, which include micro RNAs, tRNA-derived RNAs, and piwi-interacting RNA, among others. The first step to make use of these reads is to map them to a genome. Existing mapping tools have been developed for long RNAs in mind, and, so far, no tool has been conceived for short RNAs. However, short RNAs have several distinctive features which make them different from messenger RNAs: they are shorter, they are often redundant, they can be produced by duplicated loci, and they may be edited at their ends.

resultsIn this work, we present a new tool, srnaMapper, that exhaustively maps these reads with all these features in mind, and is most efficient when applied to reads no longer than 50 base pairs. We show, on several datasets, that srnaMapper is very efficient considering computation time and edition error handling: it retrieves all the hits, with arbitrary number of errors, in time comparable with non-exhaustive tools.

Indexed as

High-Throughput Nucleotide SequencingMicroRNAsRNA, Small InterferingRNA, TransferSequence Analysis, RNAMicroRNAsRNA, Small InterferingRNA, TransferMappingSequencingSRNA

Identifiers

PMID36401177
PMCPMC9675193
OpenAlexW4309467323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.