Paper Info
Title
IPromoter-2L: A two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Abstract
Motivation: Being responsible for initiating transaction of a particular gene in genome, promoter is a short region of DNA. Promoters have various types with different functions. Owing to their importance in biological process, it is highly desired to develop computational tools for timely identifying promoters and their types. Such a challenge has become particularly critical and urgent in facing the avalanche of DNA sequences discovered in the postgenomic age. Although some prediction methods were developed, they can only be used to discriminate a specific type of promoters from non-promoters. None of them has the ability to identify the types of promoters. This is due to the facts that different types of promoters may share quite similar consensus sequence pattern, and that the promoters of same type may have considerably different consensus sequences. Results: To overcome such difficulty, using the multi-window-based PseKNC (pseudo K-tuple nucleotide composition) approach to incorporate the short-, middle-, and long-range sequence information, we have developed a two-layer seamless predictor named as 'iPromoter-2 L'. The first layer serves to identify a query DNA sequence as a promoter or non-promoter, and the second layer to predict which of the following six types the identified promoter belongs to: r24, r28, r32, r38, r54 and r70. Availability and implementation: For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the powerful new predictor has been established at http://bioinformatics.hitsz.edu.cn/iPromoter-2L/. It is anticipated that iPromoter-2 L will become a very useful high throughput tool for genome analysis. Contact: bliu@hit.edu.cn or dshuang@tongji.edu.cn or kcchou@gordonlifescience.org Supplementary information: Supplementary data are available at Bioinformatics online.
Year
DOI
Venue
2018
10.1093/bioinformatics/btx579
BIOINFORMATICS
Field
DocType
Volume
Data mining,Promoter,Computer science
Journal
34
Issue
ISSN
Citations 
1
1367-4803
16
PageRank 
References 
Authors
0.66
11
4
Name
Order
Citations
PageRank
Bin Liu141933.30
Yang Fan2160.66
De-Shuang Huang35532357.50
Kuo-Chen Chou494664.26