A review of methods and databases for metagenomic classification and assembly

Abstract Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review...

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Vydané v:	Briefings in bioinformatics Ročník 20; číslo 4; s. 1125 - 1136
Hlavní autori:	Breitwieser, Florian P, Lu, Jennifer, Salzberg, Steven L
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 19.07.2019 Oxford Publishing Limited (England)
Predmet:	Algorithms Assembly Classification Computational Biology - methods Databases, Genetic - statistics & numerical data Gene Expression Profiling - statistics & numerical data Genetic Markers Genomes High-Throughput Nucleotide Sequencing - statistics & numerical data Metagenome Metagenomics Metagenomics - methods Metagenomics - statistics & numerical data Microbiomes Microbiota - genetics Phylogeny Sequence Alignment - statistics & numerical data databases bacteria next-generation sequencing microbiome microbial genomics
ISSN:	1467-5463, 1477-4054, 1477-4054
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Popis
Shrnutí:	Abstract Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbx120