FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares

Silva, Genivaldo Gueiros Z.; Cuevas, Daniel A.; Dutilh, Bas E.; Edwards, Robert A.

doi:10.7717/peerj.425

Cited by 90 publications

(75 citation statements)

References 28 publications

(33 reference statements)

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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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“…Given the variety of use-cases, it is important to understand the benefits and drawbacks of the particular taxonomic profiler for different applications. While there has been much effort in developing taxonomic profiling methods [1][2][3][4][5][6][7][8][9][10][11][12], only recently have community efforts arisen to perform unbiased comparisons of such techniques and assess their strengths and weaknesses [13,14]. Critical obstacles to such comparisons have been a lack of consensus on performance metrics and output formats by the community, as different taxonomic profilers report their results in a variety of formats and interested parties had to implement their own metrics for comparisons.…”

Section: Introductionmentioning

confidence: 99%

Assessing taxonomic metagenome profilers with OPAL

Meyer¹,

Bremges²,

Belmann

et al. 2018

Preprint

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Taxonomic metagenome profilers predict the presence and relative abundance of microorganisms from shotgun sequence samples of DNA isolated directly from a microbial community. Over the past years, there has been an explosive growth of software and algorithms for this task, resulting in a need for more systematic comparisons of these methods based on relevant performance criteria. Here, we present OPAL, a software package implementing commonly used performance metrics, including those of the first challenge of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI), together with convenient visualizations. In addition, OPAL implements diversity metrics from microbial ecology, as well as run time and memory efficiency measurements. By allowing users to customize the relative importance of metrics, OPAL facilitates in-depth performance comparisons, as well as the development of new methods and data analysis workflows. To demonstrate the application, we compared seven profilers on benchmark datasets of the first and second CAMI challenges using all metrics and performance measurements available in OPAL. The software is implemented in Python 3 and available under the Apache 2.0 license on GitHub (https://github.com/CAMI-challenge/OPAL). Author summaryThere are many computational approaches for inferring the presence and relative abundance of taxa (i.e. taxonomic profiling) from shotgun metagenome samples of microbial communities, making systematic performance evaluations a very important task. However, there has yet to be introduced a computational framework in which profiler performances can be compared. This delays method development and applied studies, as researchers need to implement their own custom evaluation frameworks. Here, we present OPAL, a software package that facilitates standardized comparisons of taxonomic metagenome profilers. It implements a variety of performance metrics July 19, 2018 1/15 frequently employed in microbiome research, including runtime and memory usage, and generates comparison reports and visualizations. OPAL thus facilitates and accelerates benchmarking of taxonomic profiling techniques on ground truth data. This enables researchers to arrive at informed decisions about which computational techniques to use for specific datasets and research questions.

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“…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%

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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FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares

Cited by 90 publications

References 28 publications

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software

Assessing taxonomic metagenome profilers with OPAL

Finer Metagenomic Reconstruction via Biodiversity Optimization

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