Literature DB >> 16769690

Novel phylogenetic studies of genomic sequence fragments derived from uncultured microbe mixtures in environmental and clinical samples.

Takashi Abe1, Hideaki Sugawara, Makoto Kinouchi, Shigehiko Kanaya, Toshimichi Ikemura.   

Abstract

A self-organizing map (SOM) was developed as a novel bioinformatics strategy for phylogenetic classification of sequence fragments obtained from pooled genome samples of uncultured microbes in environmental and clinical samples. This phylogenetic classification was possible without either orthologous sequence sets or sequence alignments. We first constructed SOMs for tetranucleotide frequencies in 210,000 5 kb sequence fragments obtained from 1502 prokaryotes for which at least 10 kb of genomic sequence has been deposited in public DNA databases. The sequences could be classified primarily according to phylogenetic groups without information regarding the species. We used the SOM method to classify sequence fragments derived from environmental samples of the Sargasso Sea and of an acidophilic biofilm growing in acid mine drainage. Phylogenetic diversity of the environmental sequences was effectively visualized on a single map. Sequences that were derived from a single genome but cloned independently could be reassociated in silico. G + C% has been used for a long period as a fundamental parameter for phylogenetic classification of microbes, but the G + C% is apparently too simple a parameter to differentiate a wide variety of known species. Oligonucleotide frequency can be used to distinguish the species because oligonucleotide frequencies vary significantly among their genomes.

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Year:  2006        PMID: 16769690     DOI: 10.1093/dnares/dsi015

Source DB:  PubMed          Journal:  DNA Res        ISSN: 1340-2838            Impact factor:   4.458


  48 in total

1.  Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics.

Authors:  Marc Weber; Hanno Teeling; Sixing Huang; Jost Waldmann; Mariette Kassabgy; Bernhard M Fuchs; Anna Klindworth; Christine Klockow; Antje Wichels; Gunnar Gerdts; Rudolf Amann; Frank Oliver Glöckner
Journal:  ISME J       Date:  2010-12-16       Impact factor: 10.302

2.  Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria.

Authors:  Betsey Dexter Dyer; Michael J Kahn; Mark D Leblanc
Journal:  Archaea       Date:  2008-12       Impact factor: 3.273

Review 3.  A bioinformatician's guide to metagenomics.

Authors:  Victor Kunin; Alex Copeland; Alla Lapidus; Konstantinos Mavromatis; Philip Hugenholtz
Journal:  Microbiol Mol Biol Rev       Date:  2008-12       Impact factor: 11.056

Review 4.  Systems biology: Functional analysis of natural microbial consortia using community proteomics.

Authors:  Nathan C VerBerkmoes; Vincent J Denef; Robert L Hettich; Jillian F Banfield
Journal:  Nat Rev Microbiol       Date:  2009-03       Impact factor: 60.633

5.  Unravelling ancient microbial history with community proteogenomics and lipid geochemistry.

Authors:  Jochen J Brocks; Jillian Banfield
Journal:  Nat Rev Microbiol       Date:  2009-08       Impact factor: 60.633

6.  WebCARMA: a web application for the functional and taxonomic classification of unassembled metagenomic reads.

Authors:  Wolfgang Gerlach; Sebastian Jünemann; Felix Tille; Alexander Goesmann; Jens Stoye
Journal:  BMC Bioinformatics       Date:  2009-12-18       Impact factor: 3.169

7.  Distinguishing microbial genome fragments based on their composition: evolutionary and comparative genomic perspectives.

Authors:  Scott C Perry; Robert G Beiko
Journal:  Genome Biol Evol       Date:  2010-01-25       Impact factor: 3.416

Review 8.  Achievements and new knowledge unraveled by metagenomic approaches.

Authors:  Carola Simon; Rolf Daniel
Journal:  Appl Microbiol Biotechnol       Date:  2009-09-16       Impact factor: 4.813

9.  The oligonucleotide frequency derived error gradient and its application to the binning of metagenome fragments.

Authors:  Isaam Saeed; Saman K Halgamuge
Journal:  BMC Genomics       Date:  2009-12-03       Impact factor: 3.969

10.  A novel bioinformatics strategy for function prediction of poorly-characterized protein genes obtained from metagenome analyses.

Authors:  Takashi Abe; Shigehiko Kanaya; Hiroshi Uehara; Toshimichi Ikemura
Journal:  DNA Res       Date:  2009-10-03       Impact factor: 4.458

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