Evidence map›Paper›PMID 33872372›Full record

ArticleNucleic acids research2021

miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale.

Tobias Fehlmann, Fabian Kern, Omar Laham, Christina Backes, Jeffrey Solomon, Pascal Hirsch, Carsten Volz, Rolf Müller, Andreas Keller

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

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

41 citing papers in PubMed.

  1. Article
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  9. Functional characterization of the 9q34.13 locus identifiesbioRxiv : the preprint server for biology · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Non-coding RNA profiling in BRAFScientific data · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

9 authors.

Tobias FehlmannChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0003-1967-2918
Fabian KernChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0002-8223-3750
Omar LahamChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
Christina BackesChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0001-9330-9290
Jeffrey SolomonChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
Pascal HirschChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.
Carsten VolzDepartment of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) and Department of Pharmacy, Saarland University, Campus E8 1, 66123 Saarbrücken, Germany.
Rolf MüllerDepartment of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) and Department of Pharmacy, Saarland University, Campus E8 1, 66123 Saarbrücken, Germany.
Andreas KellerChair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0002-5361-0895

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.

Indexed as

AnimalsCattleDementiaDogsHumansMiceMicroRNAsRatsRNA, Small UntranslatedSequence Analysis, RNASoftwareMicroRNAsRNA, Small Untranslated

Identifiers

PMID33872372
PMCPMC8262700

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.