Literature DB >> 20428222

Comparison of multiple metagenomes using phylogenetic networks based on ecological indices.

Suparna Mitra1, Jack A Gilbert, Dawn Field, Daniel H Huson.   

Abstract

Second-generation sequencing technologies are fueling a vast increase in the number and scope of metagenome projects. There is a great need for the development of new methods for visualizing the relationships between multiple metagenomic data sets. To address this, a novel approach is presented that combines the use of taxonomic analysis, ecological indices and non-hierarchical clustering to provide a network representation of the relationships between different metagenome data sets. The approach is illustrated using several published data sets of different types, including metagenomes, metatranscriptomes and 16S ribosomal profiles. Application of the approach to the same data summarized at different taxonomical levels gives rise to remarkably similar networks, indicating that the analysis is very robust. Importantly, the networks provide the both visual definition and metric quantification for the non-rooted relationship between samples, combining the desirable characteristics of other tools into one.

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Year:  2010        PMID: 20428222     DOI: 10.1038/ismej.2010.51

Source DB:  PubMed          Journal:  ISME J        ISSN: 1751-7362            Impact factor:   10.302


  16 in total

1.  Fine-scale bacterial beta diversity within a complex ecosystem (Zodletone Spring, OK, USA): the role of the rare biosphere.

Authors:  Noha H Youssef; M B Couger; Mostafa S Elshahed
Journal:  PLoS One       Date:  2010-08-26       Impact factor: 3.240

2.  Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG.

Authors:  Suparna Mitra; Paul Rupek; Daniel C Richter; Tim Urich; Jack A Gilbert; Folker Meyer; Andreas Wilke; Daniel H Huson
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

3.  Substrate type determines metagenomic profiles from diverse chemical habitats.

Authors:  Thomas C Jeffries; Justin R Seymour; Jack A Gilbert; Elizabeth A Dinsdale; Kelly Newton; Sophie S C Leterme; Ben Roudnew; Renee J Smith; Laurent Seuront; James G Mitchell
Journal:  PLoS One       Date:  2011-09-23       Impact factor: 3.240

4.  Metagenomic biomarker discovery and explanation.

Authors:  Nicola Segata; Jacques Izard; Levi Waldron; Dirk Gevers; Larisa Miropolsky; Wendy S Garrett; Curtis Huttenhower
Journal:  Genome Biol       Date:  2011-06-24       Impact factor: 13.583

5.  A poor man's BLASTX--high-throughput metagenomic protein database search using PAUDA.

Authors:  Daniel H Huson; Chao Xie
Journal:  Bioinformatics       Date:  2013-05-07       Impact factor: 6.937

6.  Potential benefits of the application of yeast starters in table olive processing.

Authors:  Francisco N Arroyo-López; Verónica Romero-Gil; Joaquín Bautista-Gallego; Francisco Rodríguez-Gómez; Rufino Jiménez-Díaz; Pedro García-García; Amparo Querol; Antonio Garrido-Fernández
Journal:  Front Microbiol       Date:  2012-04-27       Impact factor: 5.640

7.  The structure of microbial community and degradation of diatoms in the deep near-bottom layer of Lake Baikal.

Authors:  Yulia R Zakharova; Yuri P Galachyants; Maria I Kurilkina; Alexander V Likhoshvay; Darya P Petrova; Sergey M Shishlyannikov; Nikolai V Ravin; Andrey V Mardanov; Alexey V Beletsky; Yelena V Likhoshway
Journal:  PLoS One       Date:  2013-04-01       Impact factor: 3.240

8.  Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset.

Authors:  Peter E Larsen; Frank R Collart; Dawn Field; Folker Meyer; Kevin P Keegan; Christopher S Henry; John McGrath; John Quinn; Jack A Gilbert
Journal:  Microb Inform Exp       Date:  2011-06-14

9.  DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.

Authors:  Xiao Ding; Fudong Cheng; Changchang Cao; Xiao Sun
Journal:  BMC Bioinformatics       Date:  2015-10-07       Impact factor: 3.169

10.  A metagenomics transect into the deepest point of the Baltic Sea reveals clear stratification of microbial functional capacities.

Authors:  Petter Thureborn; Daniel Lundin; Josefin Plathan; Anthony M Poole; Britt-Marie Sjöberg; Sara Sjöling
Journal:  PLoS One       Date:  2013-09-23       Impact factor: 3.240

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