Publication detail

Bipartite Graphs for Metagenomic Data Analysis and Visualization

SEDLÁŘ, K. ŠKUTKOVÁ, H. VÍDEŇSKÁ, P. RYCHLÍK, I. PROVAZNÍK, I.

Original Title

Bipartite Graphs for Metagenomic Data Analysis and Visualization

Czech Title

Bipartitní grafy pro analýzu a vizualizaci metagenomických dat

English Title

Bipartite Graphs for Metagenomic Data Analysis and Visualization

Type

conference paper

Language

en

Original Abstract

Metagenomics became very popular after expansion of next-generation sequencing techniques that allowed simple implementation of extensive studies. With a target gene sequencing approach, an identification of organisms in a metagenome is quite effortless since only a small reference database of the particular gene is needed. Moreover, by counting the copies of individual genes, also quantitative analysis can be applied. Unfortunately, current bioinformatics tools aim mainly on the analysis of a single metagenome. A cluster analysis, a heatmap of correlation coefficients, biclustering or other statistics techniques can only show relations inside the metagenome or the relation between the metagenome composition and other parameters. On the other hand, there is a lack of tools to provide a comparative analysis of two or more metagenomes. Suitable properties for this kind of analysis can be found in a bipartite graph. Here, we present a novel workflow for finding the suitable representation of metagenomic data to provide a comparative analysis of metagenomes. The resulting graph can take into account information about the actual composition of the metagenome as well as the environment it relates to. Thus, it can provide different view of the data to the naked eye that can complement other techniques such as principal coordinate analysis.

Czech abstract

Metagenomice stal se velmi populární po rozšíření sekvencování techniky nové generace, které umožnily jednoduchou implementaci rozsáhlých studií. S sekvenční přístup cílového genu, identifikaci organismů v metagenomu je poměrně snadné, protože je potřeba pouze malá referenční databáze konkrétního genu. Navíc tím, že počítání kopií jednotlivých genů, i kvantitativní analýza může být použita. Bohužel stávající bioinformatiky nástroje mají za cíl hlavně na základě analýzy jednoho metagenomu. Analýza clusteru, je heatmap z korelačních koeficientů, biclustering nebo techniky, ostatní statistiky mohou ukázat pouze vztahy uvnitř metagenomu nebo vztahu mezi metagenomu složení a další parametry. Na druhé straně, je zde nedostatek nástrojů pro srovnávací analýzu ze dvou nebo více metagenomes. Mezi vhodné vlastnosti pro tento druh analýzy lze nalézt v bipartitního grafu. Zde Vám představujeme nový pracovní postup pro nalezení vhodného reprezentace metagenomic údajů poskytnout srovnávací analýzu metagenomes. Výsledný graf může vzít v úvahu informace o skutečném složení metagenomu, jakož i prostředí, ve kterém se týká. Tak, to může poskytnout jiný pohled dat pouhým okem, které může doplňovat další techniky, jako je hlavní koordinovat analýzu.

English abstract

Metagenomics became very popular after expansion of next-generation sequencing techniques that allowed simple implementation of extensive studies. With a target gene sequencing approach, an identification of organisms in a metagenome is quite effortless since only a small reference database of the particular gene is needed. Moreover, by counting the copies of individual genes, also quantitative analysis can be applied. Unfortunately, current bioinformatics tools aim mainly on the analysis of a single metagenome. A cluster analysis, a heatmap of correlation coefficients, biclustering or other statistics techniques can only show relations inside the metagenome or the relation between the metagenome composition and other parameters. On the other hand, there is a lack of tools to provide a comparative analysis of two or more metagenomes. Suitable properties for this kind of analysis can be found in a bipartite graph. Here, we present a novel workflow for finding the suitable representation of metagenomic data to provide a comparative analysis of metagenomes. The resulting graph can take into account information about the actual composition of the metagenome as well as the environment it relates to. Thus, it can provide different view of the data to the naked eye that can complement other techniques such as principal coordinate analysis.

Keywords

metagenomics; 16S rRNA; bipartite graph; visualization

RIV year

2015

Released

09.11.2015

Publisher

IEEE

Location

Washington D.C.

ISBN

978-1-4673-6798-1

Book

Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015)

Edition

1

Edition number

1

Pages from

1123

Pages to

1128

Pages count

6

BibTex


@inproceedings{BUT118376,
  author="Karel {Sedlář} and Helena {Škutková} and Petra {Vídeňská} and Ivan {Rychlík} and Ivo {Provazník}",
  title="Bipartite Graphs for Metagenomic Data Analysis and Visualization",
  annote="Metagenomics became very popular after expansion of next-generation sequencing techniques that allowed simple implementation of extensive studies. With a target gene sequencing approach, an identification of organisms in a metagenome is quite effortless since only a small reference database of the particular gene is needed. Moreover, by counting the copies of individual genes, also quantitative analysis can be applied. Unfortunately, current bioinformatics tools aim mainly on the analysis of a single metagenome. A cluster analysis, a heatmap of correlation coefficients, biclustering or other statistics techniques can only show relations inside the metagenome or the relation between the metagenome composition and other parameters. On the other hand, there is a lack of tools to provide a comparative analysis of two or more metagenomes. Suitable properties for this kind of analysis can be found in a bipartite graph. Here, we present a novel workflow for finding the suitable representation of metagenomic data to provide a comparative analysis of metagenomes. The resulting graph can take into account information about the actual composition of the metagenome as well as the environment it relates to. Thus, it can provide different view of the data to the naked eye that can complement other techniques such as principal coordinate analysis.",
  address="IEEE",
  booktitle="Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015)",
  chapter="118376",
  edition="1",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2015",
  month="november",
  pages="1123--1128",
  publisher="IEEE",
  type="conference paper"
}