Bracken: estimating species abundance in metagenomics data

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.

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

“…For example, Lindgreen, et al [13] evaluated a set of 14 metagenomics tools, using six datasets comprising more than 400 genera, with the analysis limited to phyla and genera. A similar study by Peabody, et al [21] evaluated algorithms to the species level but included only two datasets representing 11 species, without taking into account the evolution of the taxonomy of those species [31]. Meanwhile, the number of published tools for the identification of microorganisms continues to increase.…”

PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

“…While we assume that this form of presentation allows users to come to the right conclusions very quickly, more sophisticated methods for the abundance estimation especially on strain level exist. Implementing an additional abundance estimation approach comparable to the read reassignment of Clinical Pathoscope [36] or the abundance estimation of Bracken [66] could enable more accurate results, albeit this would not be applicable trivially to the overall conception of PathoLive. The sensitivity and specificity of PathoLive varies with the time of a sequencing run.…”

“…We compared the results of PathoLive to two existing solutions, Clinical Pathoscope [36] and Bracken [66]. We selected Clinical Pathoscope for its very sophisticated read reassignment method, which promises a highly reliable rating of candidate hits.…”

DNA Sequencing as a Tool to Monitor Marine Ecological Status

“…Some studies report adequate estimates of relative abundance (Aylagas et al, 2014(Aylagas et al, , 2016Elbrecht and Leese, 2015;Tan et al, 2015;Thomsen and Willerslev, 2015) even though read abundance does not necessarily provide a direct correlation to organism abundance because of issues that include variations in copy number, genome size, and growth condition. Bioinformatic solutions to the issue of quantification include estimation of organism abundance based on normalized read counts of clade-specific marker genes Sohn et al, 2014), polymorphisms in universal markers (Luo et al, 2015), or accounting for incorrect taxonomic classification through probabilistic models (Lu et al, 2017). Differences in sequencing depth, i.e., the number of reads obtained from an environmental sample, significantly affects results (Rodriguez and Konstantinides, 2014) and can potentially render a study unable to detect low-abundance strains.…”