Pseudoalignment for metagenomic read assignment

Schaeffer, Lorian; Pimentel, Harold; Bray, Nicolas; Melsted, Páll; Pachter, Lior

doi:10.1093/bioinformatics/btx106

Cited by 105 publications

(111 citation statements)

References 32 publications

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“…Therefore, any assumption that Kraken's raw read assignments can be directly translated into species-or strain-level abundance estimates (e.g., Schaeffer et al, 2015) is flawed, as ignoring reads at higher levels of the taxonomy will grossly underestimate some species, and creates the erroneous impression that Kraken's assignments themselves were incorrect.…”

Section: Classification Versus Abundance Estimationmentioning

confidence: 99%

Bracken: estimating species abundance in metagenomics data

Breitwieser²,

Thielen

et al. 2017

PeerJ Computer Science

1,239

745

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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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Section: Classification Versus Abundance Estimationmentioning

confidence: 99%

Bracken: estimating species abundance in metagenomics data

Breitwieser²,

Thielen

et al. 2017

PeerJ Computer Science

1,239

745

View full text Add to dashboard Cite

show abstract

“…We compared the performance of mSWEEP against two existing methods capable of either strain or lineage identification: metakallisto [11] and BIB [7]. The main differences between the methods are that metakallisto attempts to identify individual strains based on all available sequences, BIB uses grouped reference sequences with a single representative sequence from each group to assign abundances to the groups, and mSWEEP identifies the presence of groups by using grouped reference sequences with all the available sequence as representatives.…”

Section: Assigning Single-colony Isolates To Lineagementioning

confidence: 99%

High-resolution sweep metagenomics using fast probabilistic inference

Mäklin

Kallonen

David³

et al. 2018

Preprint

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Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

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“…They are very efficient but limited in terms of sensitivity. Metakallisto (27), a pseudo-alignment technique for metagenomic taxonomic classification, relies on the idea of approximate k-mer matching via a k-mer de Bruijn graph -an idea originally introduced by Kallisto (28) for quantifying transcript abundances in RNAseq data. Other approaches rely on supervised machine learning (ML) classifiers, such as, Naïve Bayes (NB) or support vector machines (SVM), trained on a set of reference sequences to classify taxonomic origins of metagenomic reads using their relative k-mer frequency vectors (29; 30).…”

Section: Introductionmentioning

confidence: 99%

Carnelian: alignment-free functional binning and abundance estimation of metagenomic reads

Nazeen

Berger

2018

Preprint

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Accurate assignment of metagenomic reads to their functional roles is an important first step towards gaining insights into the relationship between the human microbiomeincluding the collective genesand disease. Existing approaches focus on binning sequencing reads into known taxonomic classes or by genes, often failing to produce results that generalize across different cohorts with the same disease. We present Carnelian, a highly precise and accurate pipeline for alignment-free functional binning and abundance estimation, which leverages the recent idea of even-coverage, lowdensity locality sensitive hashing. When coupled with one-against-all classifiers, reads can be binned by molecular function encoded in their gene content with higher precision and accuracy. Carnelians minutes-per-metagenome processing speed enables analysis of large-scale disease or environmental datasets to reveal disease-and environment-specific changes in microbial functionality previously poorly understood. Our pipeline newly reveals a functional dysbiosis in patient gut microbiomes, not found in earlier metagenomic studies, and identifies a distinct shift from matched healthy individuals in Type-2 Diabetes (T2D) and early-stage Parkinson's Disease (PD). We remarkably identify a set of functional markers that can differentiate between patients and healthy individuals consistently across both the datasets with high specificity.

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Pseudoalignment for metagenomic read assignment

Cited by 105 publications

References 32 publications

Bracken: estimating species abundance in metagenomics data

Bracken: estimating species abundance in metagenomics data

High-resolution sweep metagenomics using fast probabilistic inference

Carnelian: alignment-free functional binning and abundance estimation of metagenomic reads

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