Publication detail

Signal processing based CNV detection in bacterial genomes

JUGAS, R. VÍTEK, M. MADĚRÁNKOVÁ, D. ŠKUTKOVÁ, H.

Original Title

Signal processing based CNV detection in bacterial genomes

English Title

Signal processing based CNV detection in bacterial genomes

Type

conference paper

Language

en

Original Abstract

Copy number variation (CNV) plays important role in drug resistance in bacterial genomes. It is one of the prevalent forms of structural variations which leads to duplications or deletions of regions with varying size across the genome. So far, most studies were concerned with CNV in eukaryotic, mainly human, genomes. The traditional laboratory methods as microarray genome hybridization or genotyping methods are losing its effectiveness with the omnipotent increase of fully sequenced genomes. Methods for CNV detection are predominantly targeted at eukaryotic sequencing data and only a few of tools is available for CNV detection in prokaryotic genomes. In this paper, we propose the CNV detection algorithm derived from state-of-the-art methods for peaks detection in the signal processing domain. The modified method of GC normalization with higher resolution is also presented for the needs of the CNV detection. The performance of the algorithms are discussed and analyzed.

English abstract

Copy number variation (CNV) plays important role in drug resistance in bacterial genomes. It is one of the prevalent forms of structural variations which leads to duplications or deletions of regions with varying size across the genome. So far, most studies were concerned with CNV in eukaryotic, mainly human, genomes. The traditional laboratory methods as microarray genome hybridization or genotyping methods are losing its effectiveness with the omnipotent increase of fully sequenced genomes. Methods for CNV detection are predominantly targeted at eukaryotic sequencing data and only a few of tools is available for CNV detection in prokaryotic genomes. In this paper, we propose the CNV detection algorithm derived from state-of-the-art methods for peaks detection in the signal processing domain. The modified method of GC normalization with higher resolution is also presented for the needs of the CNV detection. The performance of the algorithms are discussed and analyzed.

Keywords

CNV; copy number variant; bacterial genomes; signal processing; sequencing

Released

13.04.2019

Publisher

Springer Verlag

Location

Granada, Spain

ISBN

978-3-030-17937-3

Book

Bioinformatics and Biomedical Engineering. IWBBIO 2019.

Pages from

93

Pages to

102

Pages count

10

URL

BibTex


@inproceedings{BUT157929,
  author="Robin {Jugas} and Martin {Vítek} and Denisa {Maděránková} and Helena {Škutková}",
  title="Signal processing based CNV detection in bacterial genomes",
  annote="Copy number variation (CNV) plays important role in drug resistance in bacterial genomes. It is one of the prevalent forms of structural variations which leads to duplications or deletions of regions with varying size across the genome. So far, most studies were concerned with CNV in eukaryotic, mainly human, genomes. The traditional laboratory methods as microarray genome hybridization or genotyping methods are losing its effectiveness with the omnipotent increase of fully sequenced genomes. Methods for CNV detection are predominantly targeted at eukaryotic sequencing data and only a few of tools is available for CNV detection in prokaryotic genomes. In this paper, we propose the CNV detection algorithm derived from state-of-the-art methods for peaks detection in the signal processing domain. The modified method of GC normalization with higher resolution is also presented for the needs of the CNV detection. The performance of the algorithms are discussed and analyzed.",
  address="Springer Verlag",
  booktitle="Bioinformatics and Biomedical Engineering. IWBBIO 2019.",
  chapter="157929",
  doi="10.1007/978-3-030-17938-0_9",
  howpublished="print",
  institution="Springer Verlag",
  number="11465",
  year="2019",
  month="april",
  pages="93--102",
  publisher="Springer Verlag",
  type="conference paper"
}