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Related Experiment Video

Updated: Jan 3, 2026

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Improved metagenomic analysis with Kraken 2.

Derrick E Wood^1,2, Jennifer Lu^2,3, Ben Langmead^4,5

¹Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.

|November 30, 2019

Summary

Kraken 2 significantly reduces memory usage and increases speed for metagenomic taxonomic classification. This enhanced tool improves viral metagenomics analysis sensitivity and allows for larger reference datasets.

Keywords:

Alignment-free methods Metagenomics Metagenomics classification Microbiome Minimizers Probabilistic data structures

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Last Updated: Jan 3, 2026

Metagenomic Analysis of Silage

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Published on: January 13, 2017

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

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Published on: April 4, 2021

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Kraken 1 offers fast k-mer-based taxonomic classification of metagenomic data.
Kraken 1's high memory requirements limit its application scope.
Metagenomic analysis demands efficient and scalable computational tools.

Purpose of the Study:

To develop an improved version of Kraken for metagenomic taxonomic classification.
To address the memory limitations of Kraken 1.
To enhance the speed and sensitivity of metagenomic analysis, particularly for viral sequences.

Main Methods:

Development of Kraken 2, an optimized k-mer-based taxonomic classifier.
Implementation of memory reduction techniques, achieving an 85% decrease compared to Kraken 1.
Introduction of a translated search mode for enhanced sensitivity.

Main Results:

Kraken 2 demonstrates a fivefold increase in speed over Kraken 1.
Kraken 2 utilizes 85% less memory, enabling the use of larger reference genomes.
The translated search mode significantly improves sensitivity in viral metagenomics.

Conclusions:

Kraken 2 provides a more memory-efficient, faster, and accurate solution for metagenomic taxonomic classification.
The enhanced sensitivity in viral metagenomics expands its utility for diverse microbial community studies.
Kraken 2 represents a substantial advancement in computational tools for analyzing large-scale genomic datasets.