Bracken: estimating species abundance in metagenomics data
Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most me...
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| Published in | PeerJ. Computer science Vol. 3; p. e104 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
PeerJ. Ltd
02.01.2017
PeerJ, Inc PeerJ Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species. |
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| AbstractList | Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species. Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species. |
| ArticleNumber | e104 |
| Audience | Academic |
| Author | Lu, Jennifer Thielen, Peter Breitwieser, Florian P. Salzberg, Steven L. |
| AuthorAffiliation | 2 Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States 3 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, United States 1 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States 4 Departments of Computer Science and Biostatistics, Johns Hopkins University, Baltimore, MD, United States |
| AuthorAffiliation_xml | – name: 1 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States – name: 4 Departments of Computer Science and Biostatistics, Johns Hopkins University, Baltimore, MD, United States – name: 2 Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States – name: 3 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, United States |
| Author_xml | – sequence: 1 givenname: Jennifer surname: Lu fullname: Lu, Jennifer organization: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States – sequence: 2 givenname: Florian P. surname: Breitwieser fullname: Breitwieser, Florian P. organization: Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States – sequence: 3 givenname: Peter surname: Thielen fullname: Thielen, Peter organization: Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, United States – sequence: 4 givenname: Steven L. surname: Salzberg fullname: Salzberg, Steven L. organization: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States, Departments of Computer Science and Biostatistics, Johns Hopkins University, Baltimore, MD, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40271438$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2017 PeerJ. Ltd. 2017 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Abundance Analysis Bacteria Bayesian analysis Bayesian estimation Deoxyribonucleic acid DNA DNA sequencing Estimation Gene sequencing Genomes Medical research Metagenomics Methods Microbial populations Microbiome Microorganisms Ribonucleic acid Ribosomal RNA RNA Species abundance Taxonomy Tuberculosis |
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| Title | Bracken: estimating species abundance in metagenomics data |
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