PMID- 29529991
OWN - NLM
STAT- MEDLINE
DCOM- 20181106
LR  - 20240314
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 19
IP  - 1
DP  - 2018 Mar 12
TI  - Inferring RNA sequence preferences for poorly studied RNA-binding proteins based 
      on co-evolution.
PG  - 96
LID - 10.1186/s12859-018-2091-8 [doi]
LID - 96
AB  - BACKGROUND: Characterizing the binding preference of RNA-binding proteins (RBP) 
      is essential for us to understand the interaction between an RBP and its RNA 
      targets, and to decipher the mechanism of post-transcriptional regulation. 
      Experimental methods have been used to generate protein-RNA binding data for a 
      number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of 
      computational methods have been developed to detect the RNA sequence or structure 
      preferences of the RBPs. However, the majority of RBPs have not yet been 
      experimentally characterized and lack RNA binding data. For these poorly studied 
      RBPs, the identification of their binding preferences cannot be performed by most 
      existing computational methods because the experimental binding data are 
      prerequisite to these methods. RESULTS: Here we propose a new method based on 
      co-evolution to predict the sequence preferences for the poorly studied RBPs, 
      waiving the requirement of their binding data. First, we demonstrate the 
      co-evolutionary relationship between RBPs and their RNA partners. We then present 
      a K-nearest neighbors (KNN) based algorithm to infer the sequence preference of 
      an RBP using only the preference information from its homologous RBPs. By 
      benchmarking against several in vitro and in vivo datasets, our proposed method 
      outperforms the existing alternative which uses the closest neighbor's preference 
      on all the datasets. Moreover, it shows comparable performance with two 
      state-of-the-art methods that require the presence of the experimental binding 
      data. Finally, we demonstrate the usage of this method to infer sequence 
      preferences for novel proteins which have no binding preference information 
      available. CONCLUSION: For a poorly studied RBP, the current methods used to 
      determine its binding preference need experimental data, which is expensive and 
      time consuming. Therefore, determining RBP's preference is not practical in many 
      situations. This study provides an economic solution to infer the sequence 
      preference of such protein based on the co-evolution. The source codes and 
      related datasets are available at https://github.com/syang11/KNN .
FAU - Yang, Shu
AU  - Yang S
AUID- ORCID: 0000-0002-8507-7191
AD  - Department of Computer Science, University of British Columbia, Vancouver, 
      Canada. syang11@cs.ubc.ca.
FAU - Wang, Junwen
AU  - Wang J
AD  - Department of Health Sciences Research, Mayo Clinic Arizona, Scottsdale, USA.
FAU - Ng, Raymond T
AU  - Ng RT
AD  - Department of Computer Science, University of British Columbia, Vancouver, 
      Canada.
LA  - eng
GR  - lsarp/Genome Canada/International
GR  - cnfn/Natural Sciences and Engineering Research Council of Canada/International
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20180312
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (RNA-Binding Proteins)
RN  - 63231-63-0 (RNA)
SB  - IM
MH  - *Algorithms
MH  - Binding Sites
MH  - *Evolution, Molecular
MH  - RNA/*chemistry/*metabolism
MH  - RNA-Binding Proteins/*chemistry/*metabolism
PMC - PMC5848454
OTO - NOTNLM
OT  - Co-evolution
OT  - K-nearest neighbors
OT  - Machine learning
OT  - RBP binding preference
COIS- ETHICS APPROVAL AND CONSENT TO PARTICIPATE: Not applicable. CONSENT FOR 
      PUBLICATION: Not applicable. COMPETING INTERESTS: The authors declare that they 
      have no competing interests. PUBLISHER'S NOTE: Springer Nature remains neutral 
      with regard to jurisdictional claims in published maps and institutional 
      affiliations.
EDAT- 2018/03/14 06:00
MHDA- 2018/11/07 06:00
PMCR- 2018/03/12
CRDT- 2018/03/14 06:00
PHST- 2017/11/07 00:00 [received]
PHST- 2018/02/28 00:00 [accepted]
PHST- 2018/03/14 06:00 [entrez]
PHST- 2018/03/14 06:00 [pubmed]
PHST- 2018/11/07 06:00 [medline]
PHST- 2018/03/12 00:00 [pmc-release]
AID - 10.1186/s12859-018-2091-8 [pii]
AID - 2091 [pii]
AID - 10.1186/s12859-018-2091-8 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2018 Mar 12;19(1):96. doi: 10.1186/s12859-018-2091-8.