Dataset Information

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation.

ABSTRACT: Various 'omics data types have been generated for Populus trichocarpa, each providing a layer of information which can be represented as a density signal across a chromosome. We make use of genome sequence data, variants data across a population as well as methylation data across 10 different tissues, combined with wavelet-based signal processing to perform a comprehensive analysis of the signature of the centromere in these different data signals, and successfully identify putative centromeric regions in P. trichocarpa from these signals. Furthermore, using SNP (single nucleotide polymorphism) correlations across a natural population of P. trichocarpa, we find evidence for the co-evolution of the centromeric histone CENH3 with the sequence of the newly identified centromeric regions, and identify a new CENH3 candidate in P. trichocarpa.

SUBMITTER: Weighill D

PROVIDER: S-EPMC6554479 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation.

Weighill Deborah D Macaya-Sanz David D DiFazio Stephen Paul SP Joubert Wayne W Shah Manesh M Schmutz Jeremy J Sreedasyam Avinash A Tuskan Gerald G Jacobson Daniel D

Frontiers in genetics 20190531

Various 'omics data types have been generated for <i>Populus trichocarpa</i>, each providing a layer of information which can be represented as a density signal across a chromosome. We make use of genome sequence data, variants data across a population as well as methylation data across 10 different tissues, combined with wavelet-based signal processing to perform a comprehensive analysis of the signature of the centromere in these different data signals, and successfully identify putative centr ...[more]

PMID: 31214244

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Project description:BackgroundMoe and Abildskov proposed the multiple wavelet hypothesis of atrial fibrillation (AF) on the basis of observations in the canine vagal nerve stimulation (VNS) AF model. Data from mapping studies in an in vitro canine AF model by Allessie et al (Allessie MA, Lammers WJEP, Bonke FIM, Hollen SJ. Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology and Arrhythmias. Orlando, FL: Grune & Stratton; 1985:265-275.) were used to evaluate the Moe/Abildskov hypothesis, which revealed that a critical number of wavelets sustained AF.ObjectiveThe purpose of this study was to reassess VNS mapping data using the same methods used by Allessie to evaluate Moe's multiple wavelet hypothesis.MethodsUsing the canine VNS AF model in 6 dogs, 510 unipolar atrial electrograms were recorded simultaneously from both atria. Activation sequence maps were produced from sustained AF during VNS in each dog. Per Allessie, consecutive 10 ms activation windows were analyzed over a period of 300 ms. Repetitive activation analysis was applied to Moe's canine VNS AF model.ResultsThe number of wavefronts in each AF episode was 0-8 in Allessie's studies measured by sequential atrial mapping and 0-10 in our biatrial simultaneous mapping studies. In both studies, an electrically silent period was observed in each atrium and was reactivated by wavefronts emanating from focal sources. Allessie postulated that an electrically silent atrium was reactivated by a wavefront propagating from the other atrium. However, in our biatrial simultaneous mapping studies, each electrically silent atrium was reactivated by a distinct focal source.ConclusionData from both studies showed a similar number of wavefronts, similar AF activation patterns, and periods of electrical atrial silence reactivated by focal sources. Also, in our studies, independent focal sources initiated wavefronts reactivating the atria, thereby explaining the mechanism maintaining AF.

Project description:MotivationDNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.ResultsWe applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.Availability and implementationAutomated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmapSupplementary informationSupplementary data are available at Bioinformatics online.Contactjefmorris@mdanderson.org.

Dataset Information

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation.

Publications

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation.

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