Literature DB >> 16524840

Comparative analysis of environmental sequences: potential and challenges.

Konrad U Foerstner1, Christian von Mering, Peer Bork.   

Abstract

Environmental sequencing, also dubbed metagenomics, is increasingly being used to obtain insights into organismal communities in diverse habitats, and has a variety of potential applications foreseeable in biotechnology and medicine. The first public large-scale data provide already a wealth of information hidden in vast amounts of fragmented pieces of DNA from unknown species residing in these environments. Comparative sequence analysis is essential for the interpretation of such data. However, different layers of complexity that are intrinsic to each sample require the establishment of some baselines for comparison: how to normalize for the differences in phylogenetic and functional diversity, how to avoid biases from incomplete data, and how to deal with differences in species dominance or genome sizes? Here we discuss a few of these items and delineate some simple discriminative sequence properties for four distinct habitats.

Mesh:

Year:  2006        PMID: 16524840      PMCID: PMC1609345          DOI: 10.1098/rstb.2005.1809

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  24 in total

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4.  Base composition bias might result from competition for metabolic resources.

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Review 5.  The uncultured microbial majority.

Authors:  Michael S Rappé; Stephen J Giovannoni
Journal:  Annu Rev Microbiol       Date:  2003       Impact factor: 15.500

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7.  Community structure and metabolism through reconstruction of microbial genomes from the environment.

Authors:  Gene W Tyson; Jarrod Chapman; Philip Hugenholtz; Eric E Allen; Rachna J Ram; Paul M Richardson; Victor V Solovyev; Edward M Rubin; Daniel S Rokhsar; Jillian F Banfield
Journal:  Nature       Date:  2004-02-01       Impact factor: 49.962

8.  Environments shape the nucleotide composition of genomes.

Authors:  Konrad U Foerstner; Christian von Mering; Sean D Hooper; Peer Bork
Journal:  EMBO Rep       Date:  2005-12       Impact factor: 8.807

9.  High guanine-cytosine content is not an adaptation to high temperature: a comparative analysis amongst prokaryotes.

Authors:  L D Hurst; A R Merchant
Journal:  Proc Biol Sci       Date:  2001-03-07       Impact factor: 5.349

10.  Metagenome survey of biofilms in drinking-water networks.

Authors:  C Schmeisser; C Stöckigt; C Raasch; J Wingender; K N Timmis; D F Wenderoth; H-C Flemming; H Liesegang; R A Schmitz; K-E Jaeger; W R Streit
Journal:  Appl Environ Microbiol       Date:  2003-12       Impact factor: 4.792

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  10 in total

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Authors:  David T Jones; Michael J E Sternberg; Janet M Thornton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-03-29       Impact factor: 6.237

2.  Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach.

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Journal:  IEEE Access       Date:  2020-10-15       Impact factor: 3.367

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Journal:  PLoS One       Date:  2010-05-12       Impact factor: 3.240

4.  An ORFome assembly approach to metagenomics sequences analysis.

Authors:  Yuzhen Ye; Haixu Tang
Journal:  J Bioinform Comput Biol       Date:  2009-06       Impact factor: 1.122

5.  The positive role of the ecological community in the genomic revolution.

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Authors:  Jeroen Raes; Jan O Korbel; Martin J Lercher; Christian von Mering; Peer Bork
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7.  A statistical toolbox for metagenomics: assessing functional diversity in microbial communities.

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Journal:  BMC Bioinformatics       Date:  2008-01-23       Impact factor: 3.169

8.  Genomics of biological wastewater treatment.

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9.  The next meta-challenge for Bioinformatics.

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10.  Binning sequences using very sparse labels within a metagenome.

Authors:  Chon-Kit Kenneth Chan; Arthur L Hsu; Saman K Halgamuge; Sen-Lin Tang
Journal:  BMC Bioinformatics       Date:  2008-04-28       Impact factor: 3.169

  10 in total

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