Evidence map›Paper›PMID 28886744›Full record

ArticleGenome biology2017

Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.

Yang Eric Li, Mu Xiao, Binbin Shi, Yu-Cheng T Yang, Dong Wang, Fei Wang, Marco Marcia, Zhi John Lu

Abstract read
In one paragraph

Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

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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

30 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

8 authors.

Yang Eric LiMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Mu XiaoLife Sciences Institute, Innovation Center for Cell Signaling Network, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Binbin ShiMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Yu-Cheng T YangMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Dong WangMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Fei WangLife Sciences Institute, Innovation Center for Cell Signaling Network, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Marco MarciaEuropean Molecular Biology Laboratory, Grenoble Outstation, 71 Avenue des Martyrs, Grenoble, 38042, France.
Zhi John LuMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China. zhilu@tsinghua.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

Indexed as

Regulatory Sequences, Nucleic AcidBinding SitesHEK293 CellsHep G2 CellsHumansK562 CellsNucleotide MotifsProtein BindingRNARNA-Binding ProteinsSequence Analysis, RNARNARNA-Binding ProteinsCLIP-seqNon-negative matrix factorizationRBPgroupRNA-binding protein

Identifiers

PMID28886744
PMCPMC5591525

What OpenQuestion holds

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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.