Detail publikace

Prokaryotic DNA Signal Downsampling for Fast Whole Genome Comparison

Originální název

Prokaryotic DNA Signal Downsampling for Fast Whole Genome Comparison

Anglický název

Prokaryotic DNA Signal Downsampling for Fast Whole Genome Comparison

Jazyk

en

Originální abstrakt

Classification of prokaryotes is mainly based on molecular data, since next-generation sequencing platforms provide fast and effective way to capture prokaryotes characteristics. However, two different bacterial strains of the same genus can differ in the specific parts of their genomes due to copious amounts of repetitive and transposable parts. Thus, finding an ideal segment of genome for comparison is difficult. Conventional character-based methods rely on multiple sequence alignment, rendering them extremely computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, we present a novel algorithm based on the conversion of the whole genome sequences to cumulative phase signals. Dyadic wavelet transform (DWT) is used for lossy compression of phase signals by eliminating redundant frequency bands. Signal classification is then performed as cluster analysis using Euclidean metrics where sequence alignment is replaced by dynamic time warping (DTW).

Anglický abstrakt

Classification of prokaryotes is mainly based on molecular data, since next-generation sequencing platforms provide fast and effective way to capture prokaryotes characteristics. However, two different bacterial strains of the same genus can differ in the specific parts of their genomes due to copious amounts of repetitive and transposable parts. Thus, finding an ideal segment of genome for comparison is difficult. Conventional character-based methods rely on multiple sequence alignment, rendering them extremely computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, we present a novel algorithm based on the conversion of the whole genome sequences to cumulative phase signals. Dyadic wavelet transform (DWT) is used for lossy compression of phase signals by eliminating redundant frequency bands. Signal classification is then performed as cluster analysis using Euclidean metrics where sequence alignment is replaced by dynamic time warping (DTW).

BibTex


@article{BUT107893,
  author="Karel {Sedlář} and Helena {Škutková} and Martin {Vítek} and Ivo {Provazník}",
  title="Prokaryotic DNA Signal Downsampling for Fast Whole Genome Comparison",
  annote="Classification of prokaryotes is mainly based on molecular data, since next-generation sequencing platforms provide fast and effective way to capture prokaryotes characteristics. However, two different bacterial strains of the same genus can differ in the specific parts of their genomes due to copious amounts of repetitive and transposable parts. Thus, finding an ideal segment of genome for comparison is difficult. Conventional character-based methods rely on multiple sequence alignment, rendering them extremely computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, we present a novel algorithm based on the conversion of the whole genome sequences to cumulative phase signals. Dyadic wavelet transform (DWT) is used for lossy compression of phase signals by eliminating redundant frequency bands. Signal classification is then performed as cluster analysis using Euclidean metrics where sequence alignment is replaced by dynamic time warping (DTW).",
  address="Springer International Publishing",
  chapter="107893",
  doi="10.1007/978-3-319-06593-9_33",
  institution="Springer International Publishing",
  number="6",
  volume="283",
  year="2014",
  month="june",
  pages="373--383",
  publisher="Springer International Publishing",
  type="journal article"
}