MULTiPly: A novel multi-layer predictor for discovering general and specific types of promoters
Date
2019
Authors
Zhang, M.
Li, F.
Marquez-Lago, T.T.
Leier, A.
Fan, C.
Kwoh, C.K.
Chou, K.C.
Song, J.
Jia, C.
Editors
Hancock, J.
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Journal article
Citation
Bioinformatics, 2019; 35(17):2957-2965
Statement of Responsibility
Meng Zhang, Fuyi Li, Tatiana T. Marquez-Lago, André Leier, Cunshuo Fan, Chee Keong Kwoh, Kuo-Chen Chou, Jiangning Song and Cangzhi Jia
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Abstract
Motivation: Promoters are short DNA consensus sequences that are localized proximal to the transcription start sites of genes, allowing transcription initiation of particular genes. However, the precise prediction of promoters remains a challenging task because individual promoters often differ from the consensus at one or more positions. Results: In this study, we present a new multi-layer computational approach, called MULTiPly, for recognizing promoters and their specific types. MULTiPly took into account the sequences themselves, including both local information such as k-tuple nucleotide composition, dinucleotidebased auto covariance and global information of the entire samples based on bi-profile Bayes and k-nearest neighbour feature encodings. Specifically, the F-score feature selection method was applied to identify the best unique type of feature prediction results, in combination with other types of features that were subsequently added to further improve the prediction performance of MULTiPly. Benchmarking experiments on the benchmark dataset and comparisons with five stateof- the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold crossvalidation and jackknife tests. Moreover, the superiority of MULTiPly was also validated on a newly constructed independent test dataset. MULTiPly is expected to be used as a useful tool that will facilitate the discovery of both general and specific types of promoters in the post-genomic era. Availability and implementation: The MULTiPly webserver and curated datasets are freely available at http://flagshipnt.erc.monash.edu/MULTiPly/. Contact: kcchou@gordonlifescience.org, Jiangning.Song@monash.edu or cangzhijia@dlmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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© The Author(s) 2019. Published by Oxford University Press. All rights reserved.