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Statistical, Visual and Functional A...

Dhariwal, Achal.

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Statistical, Visual and Functional Analysis of Microbiome Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical, Visual and Functional Analysis of Microbiome Data./
Author:	Dhariwal, Achal.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	73 p.
Notes:	Source: Masters Abstracts International, Volume: 82-09.
Contained By:	Masters Abstracts International82-09.
Subject:	Taxonomy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28249028
ISBN:	9798582580805

Statistical, Visual and Functional Analysis of Microbiome Data.
Dhariwal, Achal.

Statistical, Visual and Functional Analysis of Microbiome Data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 73 p.

Source: Masters Abstracts International, Volume: 82-09.

Thesis (M.Sc.)--McGill University (Canada), 2017.

The advancements in next-generation sequencing technologies have revolutionized microbiome research by allowing culture-independent high-throughput profiling of the genetic contents of microbial communities. Nowadays, 16S rRNA based marker gene sequencing is widely used to characterize the taxonomic composition and phylogenetic diversity of complex microbial communities. However, statistical, visual and functional analysis of such data possess great challenges. In addition, many aspects of the current approaches can be improved to get a better understanding of communities. The proper analysis of the resulting large and complicated datasets remains a key bottleneck in current microbiome studies. Over the last decade, powerful computational pipelines and standard protocols have been developed to support efficient raw data processing and annotation of microbiome data. The focus has now shifted towards downstream statistical analysis and functional interpretation.To address this bottleneck, we have developed MicrobiomeAnalyst, a user-friendly web-based tool that incorporates recent progresses in statistics and interactive visualization techniques, coupled with novel knowledge bases, to facilitate comprehensive analysis of common data sets generated from microbiome studies. MicrobiomeAnalyst contains four major components, including i) a module for community diversity profiling, comparative analysis and functional prediction of 16S rRNA marker gene data; ii) a module for exploratory data analysis, functional profiling and metabolic network visualization for shotgun metagenomics or metatranscriptomics data; iii) a module to help users to interpret their taxa of interest via enrichment analysis against ~300 taxon sets manually collected from recent literature and public databases; and iv) a module to allow users to visually explore their data sets within the context of compatible public data (meta-analysis) for pattern discovery and biological insights. The tool is freely accessible at http://www.microbiomeanalyst.ca.

ISBN: 9798582580805Subjects--Topical Terms:

3556303
Taxonomy.
Subjects--Index Terms:

Phylogenetics

Statistical, Visual and Functional Analysis of Microbiome Data.
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245 1 0 $a Statistical, Visual and Functional Analysis of Microbiome Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 73 p.
500 $a Source: Masters Abstracts International, Volume: 82-09.
500 $a Advisor: Xia, Jianguo.
502 $a Thesis (M.Sc.)--McGill University (Canada), 2017.
520 $a The advancements in next-generation sequencing technologies have revolutionized microbiome research by allowing culture-independent high-throughput profiling of the genetic contents of microbial communities. Nowadays, 16S rRNA based marker gene sequencing is widely used to characterize the taxonomic composition and phylogenetic diversity of complex microbial communities. However, statistical, visual and functional analysis of such data possess great challenges. In addition, many aspects of the current approaches can be improved to get a better understanding of communities. The proper analysis of the resulting large and complicated datasets remains a key bottleneck in current microbiome studies. Over the last decade, powerful computational pipelines and standard protocols have been developed to support efficient raw data processing and annotation of microbiome data. The focus has now shifted towards downstream statistical analysis and functional interpretation.To address this bottleneck, we have developed MicrobiomeAnalyst, a user-friendly web-based tool that incorporates recent progresses in statistics and interactive visualization techniques, coupled with novel knowledge bases, to facilitate comprehensive analysis of common data sets generated from microbiome studies. MicrobiomeAnalyst contains four major components, including i) a module for community diversity profiling, comparative analysis and functional prediction of 16S rRNA marker gene data; ii) a module for exploratory data analysis, functional profiling and metabolic network visualization for shotgun metagenomics or metatranscriptomics data; iii) a module to help users to interpret their taxa of interest via enrichment analysis against ~300 taxon sets manually collected from recent literature and public databases; and iv) a module to allow users to visually explore their data sets within the context of compatible public data (meta-analysis) for pattern discovery and biological insights. The tool is freely accessible at http://www.microbiomeanalyst.ca.
520 $a Les progres dans les technologies de sequencage de nouvelle generation ont revolutionne la recherche sur le microbiome en permettant un profilage a haut debit, independamment de la culture du contenu genetique des communautes microbiennes. De nos jours, le sequencage du gene marqueur base sur l'ARNr 16S est largement utilise pour caracteriser la composition taxonomique et la diversite phylogenetique des communautes microbiennes complexes. Cependant, l'analyse statistique, visuelle et fonctionnelle de ces donnees presente de grands defis. En outre, de nombreux aspects des approches actuelles peuvent etre ameliores pour mieux comprendre les communautes. L'analyse appropriee des donnees volumineuses et complexes reste un goulot d'etranglement majeur dans les etudes actuelles sur le microbiome. Au cours de la derniere decennie, de puissantes methodes computationnelles et des protocoles standardises ont ete developpes pour prendre en charge un traitement et une annotation des donnees efficacement. Inversement, l'accent a desormais ete mis sur l'analyse statistique en aval et l'interpretation fonctionnelle.Pour remedier a ce goulot d'etranglement, nous avons developpe MicrobiomeAnalyst, un outil web convivial qui integre les progres recents dans les statistiques et les techniques de visualisation interactives, couplees avec de nouvelles bases de connaissances, pour faciliter l'analyse complete des profils taxonomiques et fonctionnels communs issus des etudes sur le microbiome. MicrobiomeAnalyst comprend quatre modules majeurs, dont de i), un module pour le profilage de la diversite de la communaute, de l'analyse comparative et de la prediction fonctionnelle des donnees du gene marqueur de l'ARNr 16S, de ii), un module pour l'analyse exploratoire des donnees, le profilage fonctionnel et la visualisation du reseau metabolique pour les donnees de metagenomique ou de metatranscriptomique « Shotgun », de iii), un module pour aider les utilisateurs a interpreter leurs taxons d'interet par l'analyse d'enrichissement contre notre base de donnees d'environ 300 ensembles de taxons collectes manuellement a partir de la litterature recente et de bases de donnees publiques, et de iv), un module pour aider les utilisateurs a explorer visuellement leurs donnees dans le contexte de donnees publiques (meta-analyse) pour la decouverte de modeles et de connaissances biologiques. L'outil est librement accessible a http://www.microbiomeanalyst.ca.
590 $a School code: 0781.
650 4 $a Taxonomy. $3 3556303
650 4 $a Data analysis. $2 bisacsh $3 3515250
650 4 $a Genomes. $3 592593
650 4 $a Datasets. $3 3541416
650 4 $a Phylogenetics. $3 3561778
650 4 $a Metabolism. $3 541349
650 4 $a Microorganisms. $3 666946
650 4 $a Visualization. $3 586179
650 4 $a Comparative analysis. $3 3548341
650 4 $a Microbiology. $3 536250
653 $a Phylogenetics
690 $a 0410
710 2 $a McGill University (Canada). $3 1018122
773 0 $t Masters Abstracts International $g 82-09.
790 $a 0781
791 $a M.Sc.
792 $a 2017
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28249028