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, dinucleotide-based 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 state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Original language | English |
|---|---|
| Article number | btz016 |
| Pages (from-to) | 2957-2965 |
| Number of pages | 9 |
| Journal | Bioinformatics |
| Volume | 35 |
| Issue number | 17 |
| DOIs | |
| Publication status | Published - 1 Sep 2019 |
Cite this
}
MULTiPly : a novel multi-layer predictor for discovering general and specific types of promoters. / Zhang, Meng; Li, Fuyi; Marquez-Lago, Tatiana T.; Leier, Andre; Fan, Cunshuo; Kwoh, Chee Keong; Chou, Kuo-Chen; Song, Jiangning; Jia, Cangzhi .
In: Bioinformatics, Vol. 35, No. 17, btz016, 01.09.2019, p. 2957-2965.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - MULTiPly
T2 - a novel multi-layer predictor for discovering general and specific types of promoters
AU - Zhang, Meng
AU - Li, Fuyi
AU - Marquez-Lago, Tatiana T.
AU - Leier, Andre
AU - Fan, Cunshuo
AU - Kwoh, Chee Keong
AU - Chou, Kuo-Chen
AU - Song, Jiangning
AU - Jia, Cangzhi
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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, dinucleotide-based 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 state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - 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, dinucleotide-based 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 state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UR - http://www.scopus.com/inward/record.url?scp=85062570672&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btz016
DO - 10.1093/bioinformatics/btz016
M3 - Article
C2 - 30649179
AN - SCOPUS:85062570672
VL - 35
SP - 2957
EP - 2965
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 17
M1 - btz016
ER -