Literature DB >> 22227326

From genomics to metagenomics.

Narayan Desai1, Dion Antonopoulos, Jack A Gilbert, Elizabeth M Glass, Folker Meyer.   

Abstract

Next-generation sequencing has changed metagenomics. However, sequencing DNA is no longer the bottleneck, rather, the bottleneck is computational analysis and also interpretation. Computational cost is the obvious issue, as is tool limitations, considering most of the tools we routinely use have been built for clonal genomics or are being adapted to microbial communities. The current trend in metagenomics analysis is toward reducing computational costs through improved algorithms and through analysis strategies. Data sharing and interoperability between tools are critical, since computation for metagenomic datasets is very high.
Copyright © 2012 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2012        PMID: 22227326     DOI: 10.1016/j.copbio.2011.12.017

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  23 in total

1.  Evaluating techniques for metagenome annotation using simulated sequence data.

Authors:  Richard J Randle-Boggis; Thorunn Helgason; Melanie Sapp; Peter D Ashton
Journal:  FEMS Microbiol Ecol       Date:  2016-05-08       Impact factor: 4.194

Review 2.  Online tools for bioinformatics analyses in nutrition sciences.

Authors:  Sridhar A Malkaram; Yousef I Hassan; Janos Zempleni
Journal:  Adv Nutr       Date:  2012-09-01       Impact factor: 8.701

3.  Metagenomic analysis of the dust particles collected from the suction tube and the suction funnel of a dermatological laser smoke evacuator system.

Authors:  Ga-Eun Lee; Jin Ju Kim; Hei Sung Kim; Woo Jun Sul
Journal:  Lasers Med Sci       Date:  2020-10-20       Impact factor: 3.161

4.  MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.

Authors:  Martin Steinegger; Johannes Söding
Journal:  Nat Biotechnol       Date:  2017-10-16       Impact factor: 54.908

5.  MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

Authors:  Folker Meyer; Saurabh Bagchi; Somali Chaterji; Wolfgang Gerlach; Ananth Grama; Travis Harrison; Tobias Paczian; William L Trimble; Andreas Wilke
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

6.  Biochemical diversity of carboxyl esterases and lipases from Lake Arreo (Spain): a metagenomic approach.

Authors:  Mónica Martínez-Martínez; María Alcaide; Anatoli Tchigvintsev; Oleg Reva; Julio Polaina; Rafael Bargiela; María-Eugenia Guazzaroni; Alvaro Chicote; Albert Canet; Francisco Valero; Eugenio Rico Eguizabal; María del Carmen Guerrero; Alexander F Yakunin; Manuel Ferrer
Journal:  Appl Environ Microbiol       Date:  2013-03-29       Impact factor: 4.792

7.  Building a Science Gateway For Processing and Modeling Sequencing Data Via Apache Airavata.

Authors:  Zhong Wang; Marcus A Christie; Eroma Abeysinghe; Tinyi Chu; Suresh Marru; Marlon Pierce; Charles G Danko
Journal:  Pract Exp Adv Res Comput 2018 (2018)       Date:  2018-07

8.  PyroTRF-ID: a novel bioinformatics methodology for the affiliation of terminal-restriction fragments using 16S rRNA gene pyrosequencing data.

Authors:  David G Weissbrodt; Noam Shani; Lucas Sinclair; Grégory Lefebvre; Pierre Rossi; Julien Maillard; Jacques Rougemont; Christof Holliger
Journal:  BMC Microbiol       Date:  2012-12-27       Impact factor: 3.605

9.  Compareads: comparing huge metagenomic experiments.

Authors:  Nicolas Maillet; Claire Lemaitre; Rayan Chikhi; Dominique Lavenier; Pierre Peterlongo
Journal:  BMC Bioinformatics       Date:  2012-12-19       Impact factor: 3.169

10.  A pyrosequencing-based metagenomic study of methane-producing microbial community in solid-state biogas reactor.

Authors:  An Li; Ya'nan Chu; Xumin Wang; Lufeng Ren; Jun Yu; Xiaoling Liu; Jianbin Yan; Lei Zhang; Shuangxiu Wu; Shizhong Li
Journal:  Biotechnol Biofuels       Date:  2013-01-15       Impact factor: 6.040

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