Evidence map›Paper›PMID 23296921›Full record

ArticleGenome research2013

Structure-based whole-genome realignment reveals many novel noncoding RNAs.

Sebastian Will, Michael Yu, Bonnie Berger

Abstract read
In one paragraph

Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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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

3 authors.

Sebastian WillComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Michael Yu
Bonnie Berger

Funding

Structure-Based Prediction of the InteractomeR01GM081871 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2008 to 2020
$4.3M
NIGMS NIH HHS R01 GM081871NIGMS NIH HHS R01GM081871
6 · The paper itself

Abstract

Recent genome-wide computational screens that search for conservation of RNA secondary structure in whole-genome alignments (WGAs) have predicted thousands of structural noncoding RNAs (ncRNAs). The sensitivity of such approaches, however, is limited, due to their reliance on sequence-based whole-genome aligners, which regularly misalign structural ncRNAs. This suggests that many more structural ncRNAs may remain undetected. Structure-based alignment, which could increase the sensitivity, has been prohibitive for genome-wide screens due to its extreme computational costs. Breaking this barrier, we present the pipeline REAPR (RE-Alignment for Prediction of structural ncRNA), which efficiently realigns whole genomes based on RNA sequence and structure, thus allowing us to boost the performance of de novo ncRNA predictors, such as RNAz. Key to the pipeline's efficiency is the development of a novel banding technique for multiple RNA alignment. REAPR significantly outperforms the widely used predictors RNAz and EvoFold in genome-wide screens; in direct comparison to the most recent RNAz screen on D. melanogaster, REAPR predicts twice as many high-confidence ncRNA candidates. Moreover, modENCODE RNA-seq experiments confirm a substantial number of its predictions as transcripts. REAPR's advancement of de novo structural characterization of ncRNAs complements the identification of transcripts from rapidly accumulating RNA-seq data.

Indexed as

Genome, InsectAlgorithmsAnimalsCluster AnalysisComputational BiologyDrosophila melanogasterHigh-Throughput Nucleotide SequencingHumansMaleNucleic Acid ConformationNucleotide MotifsRNA, UntranslatedSensitivity and SpecificitySequence AlignmentRNA, Untranslated

Identifiers

PMID23296921
PMCPMC3668356

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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.