Kraken: ultrafast metagenomic sequence classification using exact alignments

By Derrick E. Wood, Steven L. Salzberg. Genome Biology, 2014

4,195 citations recorded by the source corpus.

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7bbac7f7916567616d933d4b2fc8b8acbbb51bdc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Molecular Biology

Citations in the corpus

Centrifuge: rapid and sensitive classification of metagenomic sequences (2016, methodology): “In contrast, the pseudoalignment approach employed in Kraken [8] processes reads far more quickly, more than 1 million reads per minute, but its exact k-mer matching algorithm requires a large index.”
Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice (2016, methodology): “Kraken (59) was used to estimate the species level abundance in each sample.”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “…can be compared directly against genomes of known taxa or can be assembled into longer segments (contigs) which can be further annotated for gene content, function, and taxonomic assignment (Segata et al., 2012; Teeling and Glöckner, 2012; Wood and Salzberg, 2014; Bengtsson-Palme et al., 2015).”
A review of methods and databases for metagenomic classification and assembly (2019, methodology): “built from multiple genomes [64] https://ccb.”
Identifying viruses from metagenomic data using deep learning (2020, background): “Kraken [10], Centrifuge [11], and MetaPhlAn [12], which were designed mainly for bacteria composition analysis though, can rapidly map reads to known viral reference genomes.”
MinION Analysis and Reference Consortium: Phase 1 data release and analysis (2015, methodology): “Taxonomic classification of all 2D reads using Kraken version 0.10.5-beta (Wood & Salzberg, 2014) found only two experiments with non-E. coli bacterial matches: P1b-Lab3-R1 had 2.3% of the reads classified as Pseudomonadales (probably Pseudomonas putida ) and P1b-Lab3-R2 had 10.7% of reads as…”
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX) (2018, background): “This uncertainty in the segmentation of sequences has made overlapping k-mers one of the most popular representations in machine learning for all areas of bio-informatics research, including proteomics 5,9 , genomics 10,11 , epigenomics 12,13 , and metagenomics 14,15 .”
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “[20], CLARK-S [21], Kraken [9], LMAT [22], and NBC [23]; and the marker-based methods GOTTCHA [24], MetaPhlAn2 [25], and PhyloSift [26].”
MetaQUAST: evaluation of metagenome assemblies (2016, methodology): “For more precise results, we would advise using MGTAXA (Williamson et al., 2012), or methods based on exact read alignments, e.g. Kraken (Wood and Salzberg, 2014) or CLARK (Ounit et al., 2015).”
MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data (2016, methodology): “Other popular standalone taxonomic analysis tools include MetaPhlAn [17], MetaPhyler [18] and Kraken [19].”
Improved metagenomic analysis with Kraken 2 (2019, methodology): “This operation serves to permute the ordering of the ℓ -mers when calculating minimizers and helps to avoid a bias toward low-complexity ℓ -mers when selecting the minimizer of a k -mer [4,15].”
American Gut: an Open Platform for Citizen Science Microbiome Research (2018, positive): “Sequences were assigned taxonomy using Kraken v1.”
Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification (2019, methodology): “With Pavian, we provide a novel interface to explore metagenomics results from the Kraken (Wood and Salzberg, 2014), KrakenUniq (Breitwieser et al.”