Detail publikačního výsledku

Signal processing based CNV detection in bacterial genomes

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

Originální název

Signal processing based CNV detection in bacterial genomes

Anglický název

Signal processing based CNV detection in bacterial genomes

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

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

Rok RIV

2020

Vydáno

13.04.2019

Nakladatel

Springer Verlag

Místo

Granada, Spain

ISBN

978-3-030-17937-3

Kniha

Bioinformatics and Biomedical Engineering. IWBBIO 2019.

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Svazek

1

Číslo

11465

Stát

Spolková republika Německo

Strany od

93

Strany do

102

Strany počet

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"
}