Key Recent Papers
JG Bragg, MA Supple, RL Andrew, JO Borevitz Genomic variation across landscapes: insights and applications New Phytologist (2015)
Grabowski PP, Morris GP, Casler MD, Borevitz JO. Population genomic variation reveals roles of history, adaptation and ploidy in switchgrass. Mol Ecol. 2014 Jun (online)
Brown TB, Cheng R, Sirault XR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO. TraitCapture: genomic and environment modelling of plant phenomic data. Curr Opin Plant Biol. 2014 Apr (online)
Li Y, Cheng R, Spokas KA, Palmer AA, Borevitz JO. Genetic Variation for Life History Sensitivity to Seasonal Warming in Arabidopsis thaliana. Genetics. Feb 2014 (online)
Xu Zhang, Ron Hauss, Justin Borevitz. Natural Genetic Variation for Growth and Development Revealed by High-Throughput Phenotyping in Arabidopsis thaliana (G3 genetics Jan 2012) (Online)
Benjamin Brachi, Geoff Morris, Justin Borevitz. Genome Wide Association Studies in Plants: The missing heritability is in the field. Genome Biology, Oct 28, 2011. (online)
Li Y, Huang Y, Bergelson J, Nordborg M, Borevitz JO. Association Mapping of Local Climate Sensitive QTL in Arabidopsis thaliana. PNAS, Nov 15, 2010. (Online)
Full Pubmed Listing
NCBI: db=pubmed; Term=borevitz,justin[Author - Full]
HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.
BMC Bioinformatics. 2019 May 16;20(1):253
Authors: Srivastava A, Karpievitch YV, Eichten SR, Borevitz JO, Lister R
BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.
RESULTS: We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism's dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME .
CONCLUSION: HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.
PMID: 31096906 [PubMed - in process]