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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| Published in | Briefings in bioinformatics Vol. 20; no. 4; pp. 1125 - 1136 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
19.07.2019
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. 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. 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.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. |
| Author | Lu, Jennifer Salzberg, Steven L Breitwieser, Florian P |
| Author_xml | – sequence: 1 givenname: Florian P surname: Breitwieser fullname: Breitwieser, Florian P – sequence: 2 givenname: Jennifer surname: Lu fullname: Lu, Jennifer – sequence: 3 givenname: Steven L orcidid: 0000-0002-8859-7432 surname: Salzberg fullname: Salzberg, Steven L email: salzberg@jhu.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29028872$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017 The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com |
| Copyright_xml | – notice: The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017 – notice: The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. – notice: The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com |
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| Keywords | databases bacteria next-generation sequencing microbiome microbial genomics |
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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.... 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... |
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| SubjectTerms | 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 |
| Title | A review of methods and databases for metagenomic classification and assembly |
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