Evidence map›Paper›PMID 27899615›Full record

ArticleNucleic acids research2017

RAID v2.0: an updated resource of RNA-associated interactions across organisms.

Ying Yi, Yue Zhao, Chunhua Li, Lin Zhang, Huiying Huang, Yana Li, Lanlan Liu, Ping Hou, Tianyu Cui, Puwen Tan and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 1 pooled it
–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

122 citing papers in PubMed, 1 synthesis or guideline pooled it.

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62 more citing papers are in PubMed but not listed here.

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

16 authors.

Ying YiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yue ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Chunhua LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Lin ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Huiying HuangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yana LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Lanlan LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Ping HouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Tianyu CuiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Puwen TanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yongfei HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Ting ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yan HuangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xiaobo LiDepartment of Pathology, Harbin Medical University, Harbin 150081, China lixiaobo@ems.hrbmu.edu.cn.
Jia YuState Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, School of Basic Sciences & Institute of Basic Medical Sciences, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China j-yu@ibms.pumc.edu.cn.
Dong WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China wangdong@ems.hrbmu.edu.cn.ORCID 0000-0001-7949-5285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA-RNA interactions and more than 1.2 million RNA-protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

Indexed as

Databases, GeneticAnimalsRNARNA-Binding ProteinsRNARNA-Binding Proteins

Identifiers

PMID27899615
PMCPMC5210540

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Registered trials

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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.