Evidence map›Paper›PMID 26388849›Full record

ArticleFrontiers in microbiology2015

Strand-specific community RNA-seq reveals prevalent and dynamic antisense transcription in human gut microbiota.

Guanhui Bao, Mingjie Wang, Thomas G Doak, Yuzhen Ye

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

16 citing papers in PubMed, 34 citations in OpenAlex.

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  15. Use of Metatranscriptomics in Microbiome Research.Bioinformatics and biology insights · 2016
    Review
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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

4 authors at 1 institution in 1 country.

Guanhui BaoSchool of Informatics and Computing, Indiana University Bloomington, IN, USA.
Mingjie WangSchool of Informatics and Computing, Indiana University Bloomington, IN, USA.
Thomas G DoakDepartment of Biology, Indiana University Bloomington, IN, USA ; National Center for Genome Analysis Support, Indiana University Bloomington, IN, USA.
Yuzhen YeSchool of Informatics and Computing, Indiana University Bloomington, IN, USA.
Indiana University Bloomington · US

Funding

Graph-centric approaches to metatranscriptomic and metaproteomic data analysisR01AI108888 · NIAID · TRUSTEES OF INDIANA UNIVERSITY · PI YE, YUZHEN · 2014 to 2017
$1.4M
NIAID NIH HHS R01 AI108888
6 · The paper itself

Abstract

Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts. Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts). Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment. As a demonstration of utilizing metatranscriptomics beyond these common analyses, we developed a computational and statistical procedure to analyze the antisense transcripts in strand-specific metatranscriptomic datasets. Antisense RNAs encoded on the DNA strand opposite a gene's CDS have the potential to form extensive base-pairing interactions with the corresponding sense RNA, and can have important regulatory functions. Most studies of antisense RNAs in bacteria are rather recent, are mostly based on transcriptome analysis, and have been applied mainly to single bacterial species. Application of our approaches to human gut-associated metatranscriptomic datasets allowed us to survey antisense transcription for a large number of bacterial species associated with human beings. The ratio of protein coding genes with antisense transcription ranges from 0 to 35.8% (median = 10.0%) among 47 species. Our results show that antisense transcription is dynamic, varying between human individuals. Functional enrichment analysis revealed a preference of certain gene functions for antisense transcription, and transposase genes are among the most prominent ones (but we also observed antisense transcription in bacterial house-keeping genes).

Indexed as

antisense RNAhuman gut microbiotametagenomemetatranscriptometransposases

Identifiers

PMID26388849
PMCPMC4555090
OpenAlexW1874993960

What OpenQuestion holds

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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.