Bracken: Estimating species abundance in metagenomics data

Lu, Jennifer; Breitwieser, Florian P.; Thielen, Peter; Salzberg, Steven L.

doi:10.1101/051813

Cited by 284 publications

(374 citation statements)

References 17 publications

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“…For example, two major challenges remain to be addressed in the statistical estimation of abundance (Lu et al 2016). First, the RefSeq database includes many genomes nearly identical to one another, which makes it extremely difficult to distinguish those genomes present in the sample from those that are not.…”

Section: Discussionmentioning

confidence: 99%

Centrifuge: rapid and sensitive classification of metagenomic sequences

et al. 2016

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Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space.

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Section: Discussionmentioning

confidence: 99%

Centrifuge: rapid and sensitive classification of metagenomic sequences

et al. 2016

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“…As a continuation of the strain-exclusion experiments, we applied Bracken 14 to the Kraken 1 and Kraken 2 results, estimating species and genus-level sequence abundance for prokaryotic species. Bracken uses a Bayesian algorithm to integrate reads Kraken classified at higher taxonomic levels into the abundance estimates.…”

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confidence: 99%

Improved metagenomic analysis with Kraken 2

Wood

Langmead

2019

Preprint

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676

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Although Kraken's k-mer-based approach provides fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed five-fold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.Assigning taxonomic labels to sequencing reads is an important part of many computational genomics pipelines for metagenomics projects. Recent years have seen several approaches to accomplish this task in a time-efficient manner 1-3 . Kraken 4 used a memory-intensive algorithm that associates short genomic substrings (k-mers) with lowest common ancestor (LCA) taxa. Kraken and related tools like KrakenUniq 5 have proven highly efficient and accurate in other tool comparisons 6,7 . But Kraken's high memory requirements force many researchers to either use a reduced-sensitivity MiniKraken database 8,9 , or to build and use many indexes over subsets of the reference sequences 10,11 . Its memory requirements can easily exceed 100 GB 7 , especially when the reference data includes large eukaryotic genomes 12,13 . Here we introduce Kraken 2, which provides a major reduction in memory usage as well as faster classification, a spaced-seed searching scheme, a translated search mode for matching in amino acid space, and continued compatibility with the Bracken 14 species-level quantification algorithm.Kraken 2 addresses the issue of large memory requirements through two changes to Kraken 1's data structures and algorithms. While Kraken 1 used a sorted list of k-mer/LCA pairs indexed by minimizers 15 , Kraken 2 introduces a probabilistic, compact hash table to map minimizers to LCAs. This table uses one-third of the memory of a standard hash table, at the cost of some specificity and accuracy. Additionally, Kraken 2 only stores minimizers (of length ℓ, ℓ ≤ k) from the reference sequence library in the data structure, whereas Kraken 1's stored all k-mers. Kraken 2's index for a reference database consisting of 9.1 Gbp of genomic sequence uses 10.6 gigabytes of memory at classification time. Kraken 1's index for the same reference set uses 72.4 gigabytes of memory for classification (Figure 1a, Supplementary Table S1). In general, a Kraken 2 database is about 15% as large as a Kraken 1 database over the same references (Supplementary Figure S1).Kraken 2's approach is faster than Kraken 1's because only the distinct minimizers from the query (read) trigger accesses to the hash table. A similar minimizer-based approach has proven useful in accelerating read alignment 16 . Kraken 2 additionally provides a hash-based filtering approach that subsamples the set of minimizer/LCA pairs included in the table, allowing the user to specify a target hash table size; smaller hash tables yield lower memory footprint and higher classification throughput at the expens...

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“…Good quality paired-end reads were analysed with the Kraken Taxonomic Sequence Classification System version 2.0 (Wood and Salzberg, 2014) using the standard Kraken database. The abundance at different taxonomic levels was determined with Bracken version 2.0 (Lu et al, 2017). Good quality paired-end reads were de novo assembled with MEGAHIT v1.1.3 for functional analysis (Li et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Soil microbial diversity drops with land‐use change in a high mountain temperate forest: a metagenomics survey

Muñoz-Arenas

Fusaro

Hernández‐Guzmán

et al. 2020

Environ Microbiol Rep

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Summary Land‐use change has been identified as the most severe threat to biodiversity. Soils are important biodiversity reservoirs, but to what extent conversion of high‐altitude temperate forest to arable land affects taxonomic and functional soil biodiversity is still largely unknown. Shotgun metagenomics was used to determine the taxonomic and functional diversity of bacteria, archaea and DNA virus in terms of effective number of species in high‐altitude temperate oak and pine‐oak forest and arable soils from Mexico. Generally, the soil ecosystem maintained its microbial species richness notwithstanding land‐use change. Archaea diversity was not affected by land‐use change, but the bacterial diversity decreased with 45–55% when the oak forest was converted to arable land and 65–75% when the pine‐oak forest was. Loss in bacterial diversity as a result of land‐use change was positively correlated (R2 = 0.41) with the 10–25% loss in functional diversity. The archaeal communities were evener than the bacterial ones, which might explain their different response to land‐use change. We expected a decrease in DNA viral communities as the bacterial diversity decreased, i.e. their potential hosts. However, a higher viral diversity was found in the arable than in the forest soils. It was found that converting high altitude oak and pine‐oak forests to arable land more than halved the bacterial diversity, but did not affect the archaeal and even increased the viral diversity.

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Bracken: Estimating species abundance in metagenomics data

Cited by 284 publications

References 17 publications

Centrifuge: rapid and sensitive classification of metagenomic sequences

Centrifuge: rapid and sensitive classification of metagenomic sequences

Improved metagenomic analysis with Kraken 2

Soil microbial diversity drops with land‐use change in a high mountain temperate forest: a metagenomics survey

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