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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Bibliographic Details
Published inBriefings in bioinformatics Vol. 20; no. 4; pp. 1125 - 1136
Main Authors Breitwieser, Florian P, Lu, Jennifer, Salzberg, Steven L
Format Journal Article
LanguageEnglish
Published England Oxford University Press 19.07.2019
Oxford Publishing Limited (England)
Subjects
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
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Summary: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.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbx120