MetaCache: context-aware classification of metagenomic reads using minhashing

Müller, André C.; Hundt, Christian; Hildebrandt, Andreas; Hankeln, Thomas; Schmidt, Bertil

doi:10.1093/bioinformatics/btx520

Cited by 50 publications

(59 citation statements)

References 22 publications

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“…File S1. Other improvements may be possible, for example allowing inexact (approximate) matching between the query and the stored k-mers, as explored in several k-mer based sequence classification tools (Müller et al, 2017;Wood and Salzberg, 2014) or in tools for sequence comparison (Horwege et al, 2014). These or other solutions, adapted to a probabilistic setting, will be examined in the future to reduce the sensitivity of RAPPAS to gaps.…”

Section: Accuracy Limitationsmentioning

confidence: 99%

“…Improvements impacting the computational efficiency (and not just accuracy) of RAPPAS are also conceivable. For example, tailored k-mer indexing techniques can be developed similarly to other recent work (Müller et al, 2017;Liu et al, 2018), and the memory footprint of the pkDB can be reduced by limiting storage to the most discriminant phylokmers (Ounit et al, 2015). Despite the many potential improvements to RAPPAS, it is already faster than other PP implementations on real datasets (Figure 4).…”

Section: Applying Rappas To "Portable" Metagenomicsmentioning

confidence: 99%

“…A faster alternative is to build a database of taxonomically-informed k-mers which are extracted from a set of reference sequences. Classification algorithms then assign a QS to the taxonomic group sharing the highest number of the k-mers (Ames et al, 2013;Wood and Salzberg, 2014;Müller et al, 2017;Liu et al, 2018). All of these approaches use simple sequence similarity metrics and no probabilistic modeling of the evolutionary phenomena linking QSs to reference sequences.…”

Section: Introductionmentioning

confidence: 99%

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Rapid alignment-free phylogenetic identification of metagenomic sequences

Linard

Swenson

Pardi

2018

Preprint

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Motivation:Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever increasing throughput of DNA sequencing. Results: We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics. Availability: Program and sources freely available for download at gite.lirmm.fr/linard/RAPPAS.

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Section: Accuracy Limitationsmentioning

confidence: 99%

Section: Applying Rappas To "Portable" Metagenomicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rapid alignment-free phylogenetic identification of metagenomic sequences

Linard

Swenson

Pardi

2018

Preprint

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“…These methods allow very fast identification but require huge amounts of random-access-memory (RAM), sometimes more than 100 GB, especially for comprehensive databases. Other solutions try to balance between time, accuracy and memory consumption like Centrifuge (8), mash (9), sourmash (10), MetaCache (11), Ganon (12), Kaiju (13) and many more. However, due to the still growing number of reference genomes in, e.g., the NCBIs nucleotide sequence database (14), the amount of primary memory required by almost all of these tools finally scales beyond the scope of a conventional notebook.…”

Section: Introductionmentioning

confidence: 99%

kASA: Taxonomic Analysis of Metagenomic Data on a Notebook

Weging

Gogol-Döring

Große

2019

Preprint

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The taxonomic analysis of sequencing data has become important in many areas of life sciences. However, currently available software tools for that purpose either consume large amounts of RAM or yield an insufficient quality of the results. Here we present kASA, a k-mer based software capable of identifying and profiling metagenomic sequences with high computational efficiency and a small user-definable memory footprint. We ensure high sensitivity and precision via k-mers on amino acid level with a dynamic length of multiple k's. Custom algorithms and data structures that are optimised for external memory storage enable for the first time a full-scale metagenomics analysis without compromise on a standard notebook. metagenomics | taxonomic identification | taxonomic profiling | k -mers | amino acids | third-generation sequencingCorrespondence: silvio.weging@informatik.uni-halle.de

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“…Development in this area is continuing in order to increase analysis speed while reducing memory footprint. Currently, DIAMOND is one of the fastest local aligners that has a sensitivity comparable to BLAST (Buchfink et al, 2015), and MetaCache is one of the fastest and most memory e cient k-mer based classifiers, using only a discriminatory subset of available k-mers (Müller et al, 2017). All of these approaches, however, are based on sequence similarity, which can be incongruent with the true phylogenetic relationship of sequences (Smith and Pease, 2017).…”

Section: Introductionmentioning

confidence: 99%

PhyloMagnet: Fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics

Schön

Eme

Ettema

2019

Preprint

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MotivationMetagenomic and metatranscriptomic sequencing analyses have become increasingly popular tools for producing massive amounts of short-read data, often used for the reconstruction of draft genomes or the detection of (active) genes in microbial communities. Unfortunately, sequence assemblies of such datasets generally remain a computationally challenging task. Frequently, researchers are only interested in a specific group of organisms or genes; yet, the assembly of multiple datasets only to identify candidate sequences for a specific question is sometimes prohibitively slow, forcing researchers to select a subset of available datasets to address their question. Here we present PhyloMagnet, a workflow to screen meta-omics datasets for taxa and genes of interest using gene-centric assembly and phylogenetic placement of sequences.ResultsUsing PhyloMagnet, we could identify up to 87% of the genera in an in vitro mock community with variable abundances, while the false positive predictions per single gene tree ranged from 0% to 23%. When applied to a group of metagenomes for which a set of MAGs have been published, we could detect the majority of the taxonomic labels that the MAGs had been annotated with. In a metatranscriptomic setting the phylogenetic placement of assembled contigs corresponds to that of transcripts obtained from transcriptome assembly.AvailabilityPhyloMagnet is built using Nextflow, available at github.com/maxemil/PhyloMagnet and is developed and tested on Linux. It is released under the open source GNU GPL license and documentation is available at phylomagnet.readthedocs.io. Version 0.5 of PhyloMagnet was used for all benchmarks experiments.

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MetaCache: context-aware classification of metagenomic reads using minhashing

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 50 publications

References 22 publications

Rapid alignment-free phylogenetic identification of metagenomic sequences

Rapid alignment-free phylogenetic identification of metagenomic sequences

kASA: Taxonomic Analysis of Metagenomic Data on a Notebook

PhyloMagnet: Fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics

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