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

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

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.

Related collections

Most cited references 36

Record: found
Abstract: found
Article: not found

Random forest: a classification and regression tool for compound classification and QSAR modeling.

Vladimir Svetnik, Andy Liaw, Christopher Tong … (2003)

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

0 comments Cited 767 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

High-resolution mapping of meiotic crossovers and non-crossovers in yeast.

Eugenio Mancera, Richard Bourgon, Alessandro Brozzi … (2008)

Meiotic recombination has a central role in the evolution of sexually reproducing organisms. The two recombination outcomes, crossover and non-crossover, increase genetic diversity, but have the potential to homogenize alleles by gene conversion. Whereas crossover rates vary considerably across the genome, non-crossovers and gene conversions have only been identified in a handful of loci. To examine recombination genome wide and at high spatial resolution, we generated maps of crossovers, crossover-associated gene conversion and non-crossover gene conversion using dense genetic marker data collected from all four products of fifty-six yeast (Saccharomyces cerevisiae) meioses. Our maps reveal differences in the distributions of crossovers and non-crossovers, showing more regions where either crossovers or non-crossovers are favoured than expected by chance. Furthermore, we detect evidence for interference between crossovers and non-crossovers, a phenomenon previously only known to occur between crossovers. Up to 1% of the genome of each meiotic product is subject to gene conversion in a single meiosis, with detectable bias towards GC nucleotides. To our knowledge the maps represent the first high-resolution, genome-wide characterization of the multiple outcomes of recombination in any organism. In addition, because non-crossover hotspots create holes of reduced linkage within haplotype blocks, our results stress the need to incorporate non-crossovers into genetic linkage analysis.

0 comments Cited 300 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

CircNet: a database of circular RNAs derived from transcriptome sequencing data

Yu-Chen Liu, Jian-Rong Li, Chuan-Hu Sun … (2015)

Circular RNAs (circRNAs) represent a new type of regulatory noncoding RNA that only recently has been identified and cataloged. Emerging evidence indicates that circRNAs exert a new layer of post-transcriptional regulation of gene expression. In this study, we utilized transcriptome sequencing datasets to systematically identify the expression of circRNAs (including known and newly identified ones by our pipeline) in 464 RNA-seq samples, and then constructed the CircNet database (http://circnet.mbc.nctu.edu.tw/) that provides the following resources: (i) novel circRNAs, (ii) integrated miRNA-target networks, (iii) expression profiles of circRNA isoforms, (iv) genomic annotations of circRNA isoforms (e.g. 282 948 exon positions), and (v) sequences of circRNA isoforms. The CircNet database is to our knowledge the first public database that provides tissue-specific circRNA expression profiles and circRNA–miRNA-gene regulatory networks. It not only extends the most up to date catalog of circRNAs but also provides a thorough expression analysis of both previously reported and novel circRNAs. Furthermore, it generates an integrated regulatory network that illustrates the regulation between circRNAs, miRNAs and genes.

0 comments Cited 183 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Briefings in Bioinformatics

Publisher: Oxford University Press (OUP)

ISSN (Print): 1467-5463

ISSN (Electronic): 1477-4054

Publication date Created: September 2020

Publication date Created: September 25 2020

Publication date Created: October 21 2019

Publication date Other: September 2020

Publication date (Print): September 25 2020

Publication date (Electronic): October 21 2019

Volume: 21

Issue: 5

Pages: 1568-1580

Affiliations

[1 ]Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China

[2 ]Development and Planning Department, Inner Mongolia University, Hohhot 010021, China

[3 ]Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China

[4 ]Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China

Article

DOI: 10.1093/bib/bbz123

SO-VID: 9316c79a-80b8-4b33-8292-d352b02c9755

License:

https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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

Read this article at

Abstract

Related collections

Computational epistasis

Most cited references 36

Random forest: a classification and regression tool for compound classification and QSAR modeling.

High-resolution mapping of meiotic crossovers and non-crossovers in yeast.

CircNet: a database of circular RNAs derived from transcriptome sequencing data

Author and article information

Journal

Affiliations

Article

History

Comments

Comment on this article

Similar content 168

Cited by 40