Literature DB >> 31633777

A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae.

Hui Yang1, Wuritu Yang2, Fu-Ying Dao1, Hao Lv1, Hui Ding1, Wei Chen3, Hao Lin4.   

Abstract

Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  hotspots; machine learning; prediction model; recombination; sequence analysis; web server

Year:  2019        PMID: 31633777     DOI: 10.1093/bib/bbz123

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  26 in total

1.  Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Authors:  Fuyi Li; Jinxiang Chen; Zongyuan Ge; Ya Wen; Yanwei Yue; Morihiro Hayashida; Abdelkader Baggag; Halima Bensmail; Jiangning Song
Journal:  Brief Bioinform       Date:  2021-03-22       Impact factor: 11.622

2.  Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.

Authors:  Balachandran Manavalan; Shaherin Basith; Tae Hwan Shin; Gwang Lee
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

3.  i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.

Authors:  Md Mehedi Hasan; Balachandran Manavalan; Watshara Shoombuatong; Mst Shamima Khatun; Hiroyuki Kurata
Journal:  Plant Mol Biol       Date:  2020-03-05       Impact factor: 4.076

4.  HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

Authors:  Jing Li; Shida He; Fei Guo; Quan Zou
Journal:  RNA Biol       Date:  2021-02-12       Impact factor: 4.652

5.  Identification of Sub-Golgi protein localization by use of deep representation learning features.

Authors:  Zhibin Lv; Pingping Wang; Quan Zou; Qinghua Jiang
Journal:  Bioinformatics       Date:  2020-12-26       Impact factor: 6.937

6.  iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool.

Authors:  Xiao Yang; Xiucai Ye; Xuehong Li; Lesong Wei
Journal:  Front Genet       Date:  2021-03-31       Impact factor: 4.599

7.  Accurate identification of RNA D modification using multiple features.

Authors:  Lijun Dou; Wenyang Zhou; Lichao Zhang; Lei Xu; Ke Han
Journal:  RNA Biol       Date:  2021-03-17       Impact factor: 4.652

8.  4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.

Authors:  Rao Zeng; Song Cheng; Minghong Liao
Journal:  Front Cell Dev Biol       Date:  2021-05-10

9.  i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

Authors:  Yanjuan Li; Zhengnan Zhao; Zhixia Teng
Journal:  Biomed Res Int       Date:  2021-05-29       Impact factor: 3.411

10.  Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae.

Authors:  Guoqing Liu; Shuangjian Song; Qiguo Zhang; Biyu Dong; Yu Sun; Guojun Liu; Xiujuan Zhao
Journal:  Front Genet       Date:  2021-06-29       Impact factor: 4.599

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