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            -mer Painting Approach for Metagenomic Taxonomic Profiling and Quantification of Novel Strain Variation

Koslicki, David; Falush, Daniel

doi:10.1128/msystems.00020-16

Cited by 66 publications

(55 citation statements)

References 52 publications

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“…Members of the community submitted results for ten taxonomic profilers to the CAMI challenge: CLARK 32 ; ‘Common kmers’ (an early version of MetaPalette 33 , abbreviated CK in the figures); DUDes 34 ; FOCUS 35 ; MetaPhlAn 2.0 36 ; Metaphyler 37 ; mOTU 38 ; a combination of Quikr 39 , ARK 40 , and SEK 41 (abbreviated Quikr); Taxy-Pro 42 ; and TIPP 43 . For several programs, results were submitted with multiple versions or different parameter settings, bringing the number of unique submissions to twenty.…”

Section: Resultsmentioning

confidence: 99%

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

et al. 2017

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In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

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Section: Resultsmentioning

confidence: 99%

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

et al. 2017

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“…In the realistic, noisy, whole genome shotgun case where the g j correspond to whole genomes and the reads s are (short) proper subsequences of genomes, an approximate equality can also be derived (see [11] for details). Let us note that the k-mer matrix A defined in (26) is not the only possibility to arrive at (29), but serves as a prototypical example for the use of compressive-sensing-based approaches in taxonomic profiling.…”

Section: Finer Metagenomic Reconstruction Via Biodiversity Optimizationmentioning

confidence: 99%

“…There are generic algorithms based on this strategy, for instance matlab's fmincon. The values of the first and second derivatives displayed in (11) and (12) are helpful in this matter. But due to the nonconvexity, the sequence is not guaranteed to converge to a global minimizer.…”

Section: Finer Metagenomic Reconstruction Via Biodiversity Optimizationmentioning

confidence: 99%

“…Recent reviews have indicated that significant challenges exist for each of these tasks, and in particular taxonomic profiling methods still struggle to accurately characterize metagenomic samples below the genus taxonomic level [17,15]. Many of these taxonomic profiling tools attempt to determine the fewest taxa required to explain some measurement of a given metagenomic sample [4,16,18,11]. In our previous work [12,13], we introduced such a method that leverages compressive sensing techniques to find the fewest taxa that fits the frequency of short sequences of nucleotides (i.e., k-mers) in a given sample.…”

Section: Introductionmentioning

confidence: 99%

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Finer Metagenomic Reconstruction via Biodiversity Optimization

Foucart

Koslicki

2020

Preprint

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When analyzing communities of microorganisms from their sequenced DNA, an important task is taxonomic profiling: enumerating the presence and relative abundance of all organisms, or merely of all taxa, contained in the sample. This task can be tackled via compressive-sensingbased approaches, which favor communities featuring the fewest organisms among those consistent with the observed DNA data. Despite their successes, these parsimonious approaches sometimes conflict with biological realism by overlooking organism similarities. Here, we leverage a recently developed notion of biological diversity that simultaneously accounts for organism similarities and retains the optimization strategy underlying compressive-sensing-based approaches. We demonstrate that minimizing biological diversity still produces sparse taxonomic profiles and we experimentally validate superiority to existing compressive-sensing-based approaches.Despite showing that the objective function is almost never convex and often concave, generally yielding NP-hard problems, we exhibit ways of representing organism similarities for which minimizing diversity can be performed via a sequence of linear programs guaranteed to decrease diversity. Better yet, when biological similarity is quantified by k-mer co-occurrence (a popular notion in bioinformatics), minimizing diversity actually reduces to one linear program that can utilize multiple k-mer sizes to enhance performance. In proof-of-concept experiments, we verify that the latter procedure can lead to significant gains when taxonomically profiling a metagenomic sample, both in terms of reconstruction accuracy and computational performance. Reproducible code is available at https://github.com/dkoslicki/MinimizeBiologicalDiversity.

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“…Counts of all canonical 31 letter kmers were generated for each sample (BBMap v36.19; kmercountexact.sh k=31). Kmers of 31 letters were chosen because they have been shown to be able to distinguish approximate microbial species 61 . Kmers with low complexity (Shannon entropy ≤ 1.84) were removed to increase the likelihood of capturing kmers that can separate microbes at the species-level.…”

Section: Assembly Free Kmer Based Analysismentioning

confidence: 99%

Rare microbes from diverse Earth biomes dominate community activity

Sachdeva

Campbell

Heidelberg

2019

Preprint

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Microbes are the Earth's most numerous organisms and are instrumental in driving major global biological and chemical processes. Microbial activity is a crucial component of all ecosystems, as microbes have the potential to control any major biochemical process. In recent years, considerable strides have been made in describing the community structure, i.e. diversity and abundance, of microbes from the Earth's major biomes. In virtually all environments studied, a

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MetaPalette: a k -mer Painting Approach for Metagenomic Taxonomic Profiling and Quantification of Novel Strain Variation

Cited by 66 publications

References 52 publications

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Finer Metagenomic Reconstruction via Biodiversity Optimization

Rare microbes from diverse Earth biomes dominate community activity

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