Evidence map›Paper›PMID 23702463›Full record

ArticleRNA biology2013

Mapping the RNA-Seq trash bin: unusual transcripts in prokaryotic transcriptome sequencing data.

Gero Doose, Maria Alexis, Rebecca Kirsch, Sven Findeiß, David Langenberger, Rainer Machné, Mario Mörl, Steve Hoffmann, Peter F Stadler

Abstract read
In one paragraph

Article in RNA biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Circular RNAs in Eukaryotic Cells.Current genomics · 2015
    Article
  5. Review
  6. Article
  7. Origin of spliceosomal introns and alternative splicing.Cold Spring Harbor perspectives in biology · 2014
    Article
  8. Review
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.

Gero DooseBioinformatics Group; Department of Computer Science, and Interdisciplinary Center for Bioinformatics; University of Leipzig; Leipzig, Germany; Transcriptome Bioinformatics; LIFE - Leipzig Research Center for Civilization Diseases; University of Leipzig; Leipzig, Germany.
Maria Alexis
Rebecca Kirsch
Sven Findeiß
David Langenberger
Rainer Machné
Mario Mörl
Steve Hoffmann
Peter F Stadler

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prokaryotic transcripts constitute almost always uninterrupted intervals when mapped back to the genome. Split reads, i.e., RNA-seq reads consisting of parts that only map to discontiguous loci, are thus disregarded in most analysis pipelines. There are, however, some well-known exceptions, in particular, tRNA splicing and circularized small RNAs in Archaea as well as self-splicing introns. Here, we reanalyze a series of published RNA-seq data sets, screening them specifically for non-contiguously mapping reads. We recover most of the known cases together with several novel archaeal ncRNAs associated with circularized products. In Eubacteria, only a handful of interesting candidates were obtained beyond a few previously described group I and group II introns. Most of the atypically mapping reads do not appear to correspond to well-defined, specifically processed products. Whether this diffuse background is, at least in part, an incidental by-product of prokaryotic RNA processing or whether it consists entirely of technical artifacts of reverse transcription or amplification remains unknown.

Indexed as

Sequence Analysis, RNATranscriptomeArchaeaBacteriaComputational BiologyGenomicsMolecular Sequence AnnotationProkaryotic CellsRNARNAcircular sRNAsRNA-seqself-splicing intronssplit tRNAs

Identifiers

PMID23702463
PMCPMC3849169

What OpenQuestion holds

Textmetadata
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.