Literature DB >> 28686566

Guidelines to Statistical Analysis of Microbial Composition Data Inferred from Metagenomic Sequencing.

Vera Odintsova1, Alexander Tyakht2, Dmitry Alexeev2.   

Abstract

Metagenomics, the application of high-throughput DNA sequencing for surveys of environmental samples, has revolutionized our view on the taxonomic and genetic composition of complex microbial communities. An enormous richness of microbiota keeps unfolding in the context of various fields ranging from biomedicine and food industry to geology. Primary analysis of metagenomic reads allows to infer semi-quantitative data describing the community structure. However, such compositional data possess statistical specific properties that are important to be considered during preprocessing, hypothesis testing and interpreting the results of statistical tests. Failure to account for these specifics may lead to essentially wrong conclusions as a result of the survey. Here we present a researcher introduced to the field of metagenomics with the basic properties of microbial compositional data including statistical power and proposed distribution models, perform a review of the publicly available software tools developed specifically for such data and outline the recommendations for the application of the methods.

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Year:  2017        PMID: 28686566     DOI: 10.21775/cimb.024.017

Source DB:  PubMed          Journal:  Curr Issues Mol Biol        ISSN: 1467-3037            Impact factor:   2.081


  7 in total

Review 1.  Prospects of advanced metagenomics and meta-omics in the investigation of phytomicrobiome to forecast beneficial and pathogenic response.

Authors:  Atif Khurshid Wani; Nahid Akhtar; Reena Singh; Chirag Chopra; Prachi Kakade; Mahesh Borde; Jameel M Al-Khayri; Penna Suprasanna; Saurabha B Zimare
Journal:  Mol Biol Rep       Date:  2022-09-28       Impact factor: 2.742

2.  WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data.

Authors:  Joseph C Devlin; Thomas Battaglia; Martin J Blaser; Kelly V Ruggles
Journal:  BMC Genomics       Date:  2018-06-25       Impact factor: 3.969

3.  Metabarcoding assessment of prokaryotic and eukaryotic taxa in sediments from Stellwagen Bank National Marine Sanctuary.

Authors:  Jennifer M Polinski; John P Bucci; Mark Gasser; Andrea G Bodnar
Journal:  Sci Rep       Date:  2019-10-15       Impact factor: 4.379

4.  Knomics-Biota - a system for exploratory analysis of human gut microbiota data.

Authors:  Daria Efimova; Alexander Tyakht; Anna Popenko; Anatoly Vasilyev; Ilya Altukhov; Nikita Dovidchenko; Vera Odintsova; Natalya Klimenko; Robert Loshkarev; Maria Pashkova; Anna Elizarova; Viktoriya Voroshilova; Sergei Slavskii; Yury Pekov; Ekaterina Filippova; Tatiana Shashkova; Evgenii Levin; Dmitry Alexeev
Journal:  BioData Min       Date:  2018-11-06       Impact factor: 2.522

5.  IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes.

Authors:  I-Min A Chen; Ken Chu; Krishna Palaniappan; Manoj Pillay; Anna Ratner; Jinghua Huang; Marcel Huntemann; Neha Varghese; James R White; Rekha Seshadri; Tatyana Smirnova; Edward Kirton; Sean P Jungbluth; Tanja Woyke; Emiley A Eloe-Fadrosh; Natalia N Ivanova; Nikos C Kyrpides
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

6.  Meal Regularity Plays a Role in Shaping the Saliva Microbiota.

Authors:  Jannina Viljakainen; Sajan C Raju; Heli Viljakainen; Rejane Augusta de Oliveira Figueiredo; Eva Roos; Elisabete Weiderpass; Trine B Rounge
Journal:  Front Microbiol       Date:  2020-04-24       Impact factor: 5.640

7.  Analysis of the vaginal microbiome of giant pandas using metagenomics sequencing.

Authors:  Lan Zhang; Caiwu Li; Yaru Zhai; Lan Feng; Keke Bai; Zhizhong Zhang; Yan Huang; Ti Li; Desheng Li; Hao Li; Pengfei Cui; Danyu Chen; Hongning Wang; Xin Yang
Journal:  Microbiologyopen       Date:  2020-11-18       Impact factor: 3.139

  7 in total

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