Evidence map›Paper›PMID 26571139›Full record

ArticlePloS one2015

miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy.

Alexander S Baras, Christopher J Mitchell, Jason R Myers, Simone Gupta, Lien-Chun Weng, John M Ashton, Toby C Cornish, Akhilesh Pandey, Marc K Halushka

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. MicroRNA Expression Profile in Early-Stage Breast Cancers.MicroRNA (Shariqah, United Arab Emirates) · 2024
    Article
  6. Article
  7. Article
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  9. Article
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  11. Article
  12. Article
  13. Article
  14. Global MicroRNA Profiling of Vascular Endothelial Cells.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. MicroRNAs Regulating Autophagy in Neurodegeneration.Advances in experimental medicine and biology · 2021
    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.

Alexander S BarasDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Christopher J MitchellMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Jason R MyersGenomics Research Center, University of Rochester, Rochester, New York, United States of America.
Simone GuptaMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Lien-Chun WengDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
John M AshtonGenomics Research Center, University of Rochester, Rochester, New York, United States of America.
Toby C CornishDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Akhilesh PandeyMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Marc K HalushkaDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small RNA RNA-seq for microRNAs (miRNAs) is a rapidly developing field where opportunities still exist to create better bioinformatics tools to process these large datasets and generate new, useful analyses. We built miRge to be a fast, smart small RNA-seq solution to process samples in a highly multiplexed fashion. miRge employs a Bayesian alignment approach, whereby reads are sequentially aligned against customized mature miRNA, hairpin miRNA, noncoding RNA and mRNA sequence libraries. miRNAs are summarized at the level of raw reads in addition to reads per million (RPM). Reads for all other RNA species (tRNA, rRNA, snoRNA, mRNA) are provided, which is useful for identifying potential contaminants and optimizing small RNA purification strategies. miRge was designed to optimally identify miRNA isomiRs and employs an entropy based statistical measurement to identify differential production of isomiRs. This allowed us to identify decreasing entropy in isomiRs as stem cells mature into retinal pigment epithelial cells. Conversely, we show that pancreatic tumor miRNAs have similar entropy to matched normal pancreatic tissues. In a head-to-head comparison with other miRNA analysis tools (miRExpress 2.0, sRNAbench, omiRAs, miRDeep2, Chimira, UEA small RNA Workbench), miRge was faster (4 to 32-fold) and was among the top-two methods in maximally aligning miRNAs reads per sample. Moreover, miRge has no inherent limits to its multiplexing. miRge was capable of simultaneously analyzing 100 small RNA-Seq samples in 52 minutes, providing an integrated analysis of miRNA expression across all samples. As miRge was designed for analysis of single as well as multiple samples, miRge is an ideal tool for high and low-throughput users. miRge is freely available at http://atlas.pathology.jhu.edu/baras/miRge.html.

Indexed as

Sequence Analysis, RNAAnimalsBase SequenceCell DifferentiationCells, CulturedComputational BiologyEmbryonic Stem CellsEntropyHumansMiceMicroRNAsRetinal Pigment EpitheliumSoftwareMicroRNAs

Identifiers

PMID26571139
PMCPMC4646525

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.