Evidence map›Paper›PMID 22705792›Full record

ArticleNucleic acids research2012

Hybridization-based reconstruction of small non-coding RNA transcripts from deep sequencing data.

Chikako Ragan, Bryan J Mowry, Denis C Bauer

Abstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Non-coding RNA · 2022
    Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors.

Chikako RaganThe University of Queensland, Queensland Brain Institute, Brisbane, Qld. 4072, Australia. c.ragan@uq.edu.au
Bryan J Mowry
Denis C Bauer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in RNA sequencing technology (RNA-Seq) enables comprehensive profiling of RNAs by producing millions of short sequence reads from size-fractionated RNA libraries. Although conventional tools for detecting and distinguishing non-coding RNAs (ncRNAs) from reference-genome data can be applied to sequence data, ncRNA detection can be improved by harnessing the full information content provided by this new technology. Here we present NorahDesk, the first unbiased and universally applicable method for small ncRNAs detection from RNA-Seq data. NorahDesk utilizes the coverage-distribution of small RNA sequence data as well as thermodynamic assessments of secondary structure to reliably predict and annotate ncRNA classes. Using publicly available mouse sequence data from brain, skeletal muscle, testis and ovary, we evaluated our method with an emphasis on the performance for microRNAs (miRNAs) and piwi-interacting small RNA (piRNA). We compared our method with Dario and mirDeep2 and found that NorahDesk produces longer transcripts with higher read coverage. This feature makes it the first method particularly suitable for the prediction of both known and novel piRNAs.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, RNASoftwareAnimalsMiceMicroRNAsNucleic Acid ConformationNucleic Acid HybridizationRNA PrecursorsRNA, Small InterferingRNA, Small UntranslatedMicroRNAsRNA PrecursorsRNA, Small InterferingRNA, Small Untranslated

Identifiers

PMID22705792
PMCPMC3439898

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.