iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Bin Liu; Shengyu Chen; Ke Yan; Fan Weng

doi:10.3389/fgene.2019.00842

iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Front Genet. 2019 Sep 18:10:842. doi: 10.3389/fgene.2019.00842. eCollection 2019.

Authors

Bin Liu^{1

2}, Shengyu Chen³, Ke Yan⁴, Fan Weng⁴

Affiliations

¹ School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
² Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
³ School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States.
⁴ School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Abstract

Identification of replication origins is playing a key role in understanding the mechanism of DNA replication. This task is of great significance in DNA sequence analysis. Because of its importance, some computational approaches have been introduced. Among these predictors, the iRO-3wPseKNC predictor is the first discriminative method that is able to correctly identify the entire replication origins. For further improving its predictive performance, we proposed the Pseudo k-tuple GC Composition (PsekGCC) approach to capture the "GC asymmetry bias" of yeast species by considering both the GC skew and the sequence order effects of k-tuple GC Composition (k-GCC) in this study. Based on PseKGCC, we proposed a new predictor called iRO-PsekGCC to identify the DNA replication origins. Rigorous jackknife test on two yeast species benchmark datasets (Saccharomyces cerevisiae, Pichia pastoris) indicated that iRO-PsekGCC outperformed iRO-3wPseKNC. It can be anticipated that iRO-PsekGCC will be a useful tool for DNA replication origin identification. Availability and implementation: The web-server for the iRO-PsekGCC predictor was established, and it can be accessed at http://bliulab.net/iRO-PsekGCC/.

Keywords: DNA sequence analysis; pseudo k-tuple GC composition; random forest; replication origin identification; web-server.