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2019
Shokoohi, Farhad; Stephens, David A; Bourque, Guillaume; Pastinen, Tomi; Greenwood, Celia M T; Labbe, Aurélie
A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data Journal Article
In: Biometrics, vol. 75, no. 1, pp. 210–221, 2019, ISSN: 1541-0420, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12965).
Abstract | Links | BibTeX | Tags: Blood cell-separated data, Differentially methylated region, Next-generation sequencing, Read-depth
@article{shokoohi_hidden_2019,
title = {A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data},
author = {Farhad Shokoohi and David A Stephens and Guillaume Bourque and Tomi Pastinen and Celia M T Greenwood and Aurélie Labbe},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12965},
doi = {https://doi.org/10.1111/biom.12965},
issn = {1541-0420},
year = {2019},
date = {2019-01-01},
urldate = {2021-05-19},
journal = {Biometrics},
volume = {75},
number = {1},
pages = {210--221},
abstract = {DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter-CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell-separated blood methylation profiles is also provided.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12965},
keywords = {Blood cell-separated data, Differentially methylated region, Next-generation sequencing, Read-depth},
pubstate = {published},
tppubtype = {article}
}
2018
Brochu, Julien; Vlachos-Breton, Émilie; Sutherland, Sarah; Martel, Makisha; Drolet, Marc
Topoisomerases I and III inhibit R-loop formation to prevent unregulated replication in the chromosomal Ter region of Escherichia coli Journal Article
In: PLOS Genetics, vol. 14, no. 9, pp. e1007668, 2018, ISSN: 1553-7404, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: DNA amplification, DNA extraction, DNA replication, genomics, K cells, Next-generation sequencing, Phenotypes, Ribonucleases
@article{brochu_topoisomerases_2018,
title = {Topoisomerases I and III inhibit R-loop formation to prevent unregulated replication in the chromosomal Ter region of Escherichia coli},
author = {Julien Brochu and Émilie Vlachos-Breton and Sarah Sutherland and Makisha Martel and Marc Drolet},
url = {https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007668},
doi = {10.1371/journal.pgen.1007668},
issn = {1553-7404},
year = {2018},
date = {2018-01-01},
urldate = {2021-05-19},
journal = {PLOS Genetics},
volume = {14},
number = {9},
pages = {e1007668},
abstract = {Type 1A topoisomerases (topos) are the only ubiquitous topos. E. coli has two type 1A topos, topo I (topA) and topo III (topB). Topo I relaxes negative supercoiling in part to inhibit R-loop formation. To grow, topA mutants acquire compensatory mutations, base substitutions in gyrA or gyrB (gyrase) or amplifications of a DNA region including parC and parE (topo IV). topB mutants grow normally and topo III binds tightly to single-stranded DNA. What functions topo I and III share in vivo and how cells lacking these important enzymes can survive is unclear. Previously, a gyrB(Ts) compensatory mutation was used to construct topA topB null mutants. These mutants form very long filaments and accumulate diffuse DNA, phenotypes that appears to be related to replication from R-loops. Here, next generation sequencing and qPCR for marker frequency analysis were used to further define the functions of type 1A topos. The results reveal the presence of a RNase HI-sensitive origin of replication in the terminus (Ter) region of the chromosome that is more active in topA topB cells than in topA and rnhA (RNase HI) null cells. The S9.6 antibodies specific to DNA:RNA hybrids were used in dot-blot experiments to show the accumulation of R-loops in rnhA, topA and topA topB null cells. Moreover topA topB gyrB(Ts) strains, but not a topA gyrB(Ts) strain, were found to carry a parC parE amplification. When a topA gyrB(Ts) mutant carried a plasmid producing topo IV, topB null transductants did not have parC parE amplifications. Altogether, the data indicate that in E. coli type 1A topos are required to inhibit R-loop formation/accumulation mostly to prevent unregulated replication in Ter, and that they are essential to prevent excess negative supercoiling and its detrimental effects on cell growth and survival.},
note = {Publisher: Public Library of Science},
keywords = {DNA amplification, DNA extraction, DNA replication, genomics, K cells, Next-generation sequencing, Phenotypes, Ribonucleases},
pubstate = {published},
tppubtype = {article}
}