Publication result 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

Paper in proceedings (conference paper)

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

Key words in English

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

Authors

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

RIV year

2020

Released

13.04.2019

Publisher

Springer Verlag

Location

Granada, Spain

ISBN

978-3-030-17937-3

Book

Bioinformatics and Biomedical Engineering. IWBBIO 2019.

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Volume

1

Number

11465

State

Federal Republic of Germany

Pages from

93

Pages to

102

Pages count

10

URL

https://link.springer.com/chapter/10.1007/978-3-030-17938-0_9

BibTex

@inproceedings{BUT157929,
  author="Robin {Jugas} and Martin {Vítek} and Denisa {Maděránková} and Helena {Vítková}",
  title="Signal processing based CNV detection in bacterial genomes",
  booktitle="Bioinformatics and Biomedical Engineering. IWBBIO 2019.",
  year="2019",
  journal="Lecture Notes in Computer Science",
  volume="1",
  number="11465",
  pages="93--102",
  publisher="Springer Verlag",
  address="Granada, Spain",
  doi="10.1007/978-3-030-17938-0\{_}9",
  isbn="978-3-030-17937-3",
  issn="0302-9743",
  url="https://link.springer.com/chapter/10.1007/978-3-030-17938-0_9"
}