Evidence map›Paper›PMID 28377624›Full record

ArticleScientific reports2017

RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites.

Jiesi Luo, Liang Liu, Suresh Venkateswaran, Qianqian Song, Xiaobo Zhou

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.2field-weighted citation impact, top 25% 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, 31 citations in OpenAlex.

  1. Review
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  8. Computational tools to study RNA-protein complexes.Frontiers in molecular biosciences · 2022
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Jiesi LuoCenter for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
Liang LiuCenter for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
Suresh VenkateswaranCenter for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
Qianqian SongCenter for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
Xiaobo ZhouCenter for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA. xizhou@wakehealth.edu.
Wake Forest University · US

Funding

Systems Modeling Guided Bone regenerationU01AR069395 · NIAMS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI YANG, YUNZHI, ZHOU, XIAOBO · 2016 to 2021
$3.4M
Modelling the Growth of the MIC Niche at the System LevelU01CA166886 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ZHOU, XIAOBO · 2012 to 2016
$2.2M
Integrative approach to studying LncRNA functionsR01GM123037 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ZHOU, XIAOBO · 2017 to 2020
$1.5M
NCI NIH HHS U01 CA166886NIAMS NIH HHS U01 AR069395NIGMS NIH HHS R01 GM123037
6 · The paper itself

Abstract

RNA and protein interactions play crucial roles in multiple biological processes, while these interactions are significantly influenced by the structures and sequences of protein and RNA molecules. In this study, we first performed an analysis of RNA-protein interacting complexes, and identified interface properties of sequences and structures, which reveal the diverse nature of the binding sites. With the observations, we built a three-step prediction model, namely RPI-Bind, for the identification of RNA-protein binding regions using the sequences and structures of both proteins and RNAs. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously, with the results from steps 1) and 2). Compared with existing methods, most of which employ only sequences, our model significantly improves the prediction accuracy at each of the three steps. Especially, our model outperforms the catRAPID by >20% at the 3

Identifiers

PMID28377624
PMCPMC5429624
OpenAlexW2600989574

What OpenQuestion holds

Textmetadata
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.