Wavelet-based genomic signal processing for centromere identification and hypothesis generation

Deborah Weighill; David Macaya-Sanz; Stephen Paul DiFazio; Wayne Joubert; Manesh Shah; Jeremy Schmutz; Avinash Sreedasyam; Gerald Tuskan; Daniel Jacobson

doi:10.3389/fgene.2019.00487

Wavelet-based genomic signal processing for centromere identification and hypothesis generation

Deborah Weighill, David Macaya-Sanz, Stephen Paul DiFazio, Wayne Joubert, Manesh Shah, Jeremy Schmutz, Avinash Sreedasyam, Gerald Tuskan, Daniel Jacobson

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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.

Original language	English
Article number	487
Journal	Frontiers in Genetics
Volume	10
Issue number	MAY
DOIs	https://doi.org/10.3389/fgene.2019.00487
State	Published - 2019

Funding

Funding provided by The BioEnergy Science Center (BESC) and The Center for Bioenergy Innovation (CBI). U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. Support for the Poplar GWAS dataset was provided by The BioEnergy Science Center (BESC) and The Center for Bioenergy Innovation (CBI). U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science The Poplar GWAS Project used resources of the Oak Ridge Leadership Computing Facility and the Compute and Data Environment for Science at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area (http://pmi.ornl.gov) in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and by the Department of Energy, Laboratory Directed Research and Development funding (7758), at the Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. Funding provided by The BioEnergy Science Center (BESC) and The Center for Bioenergy Innovation (CBI). U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. The authors would like to acknowledge the Department of Energy Joint Genome Institute (JGI) for the sequencing of P. trichocarpa genomes, as well as Ryan McCormick, Sandra Truong, and Anna Furches for useful discussions about the manuscript. An award of computer time was provided by the INCITE program. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research also used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Keywords

CENH3
Co-evolution
DNA methylation
Data integration
Populus trichocarpa centromeres
SNP density
Wavelet transform

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10.3389/fgene.2019.00487

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title = "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.",

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AU - Sreedasyam, Avinash

AU - Tuskan, Gerald

AU - Jacobson, Daniel

N1 - Publisher Copyright: © 2019 Weighill, Macaya-Sanz, DiFazio, Joubert, Shah, Schmutz, Sreedasyam, Tuskan and Jacobson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

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Wavelet-based genomic signal processing for centromere identification and hypothesis generation

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