TaxaTarget: Fast, Sensitive, and Precise Classification of Microeukaryotes in Metagenomic Data

Abstract BackgroundMicrobial eukaryotes are nearly ubiquitous in microbiomes on Earth and contribute to many integral ecological functions. Metagenomics is a proven tool for studying the microbial diversity, functions, and ecology of microbiomes, but has been underutilized for microeukaryotes due to the computational challenges they present. For taxonomic classification, the use of a eukaryotic marker gene database can improve the computational efficiency, precision and sensitivity. However, state-of-the-art tools which use marker gene databases implement universal thresholds for classification rather than dynamically learning the thresholds from the database structure, impacting the accuracy of the classification process.ResultsHere we introduce taxaTarget, a method for the taxonomic classification of microeukaryotes in metagenomic data. Using a database of eukaryotic marker genes and a supervised learning approach for training, we learned the discriminatory power and classification thresholds for each 20 amino acid region of each marker gene in our database. This approach provided improved sensitivity and precision compared to other state-of-the-art approaches, with rapid runtimes and low memory usage. Additionally, taxaTarget was better able to detect the presence of multiple closely related species as well as species with no representative sequences in the database. One of the greatest challenges faced during the development of taxaTarget was the general sparsity of available sequences for microeukaryotes. Several algorithms were implemented, including threshold padding, which effectively handled the missing training data and reduced classification errors. Using taxaTarget on metagenomes from human fecal microbiomes, a broader range of genera were detected, including multiple parasites that the other tested tools missed.ConclusionData-driven methods for learning classification thresholds from the structure of an input database can provide granular information about the discriminatory power of the sequences and improve the sensitivity and precision of classification. These methods will help facilitate a more comprehensive analysis of metagenomic data and expand our knowledge about the diverse eukaryotes in microbial communities.

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Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arghavan Bahadorinejad ◽

Ivan Ivanov ◽

Johanna W Lampe ◽

Meredith AJ Hullar ◽

Robert S Chapkin ◽

...

Keyword(s):

16S Rrna ◽

Sample Size ◽

Microbial Communities ◽

State Of The Art ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data

Molecular Ecology Resources ◽

10.1111/1755-0998.12399 ◽

2015 ◽

Vol 15 (6) ◽

pp. 1403-1414 ◽

Cited By ~ 188

Author(s):

Johan Bengtsson-Palme ◽

Martin Hartmann ◽

Karl Martin Eriksson ◽

Chandan Pal ◽

Kaisa Thorell ◽

...

Keyword(s):

Large Subunit ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Large Subunit Rrna

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The CDD System in Computed Tomographic Diagnosis of Diverticular Disease

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/s-0043-110770 ◽

2017 ◽

Vol 189 (08) ◽

pp. 740-747 ◽

Cited By ~ 2

Author(s):

Daniel Pustelnik ◽

Fabian Elsholtz ◽

Christian Bojarski ◽

Bernd Hamm ◽

Stefan Niehues

Keyword(s):

Computed Tomography ◽

Decision Making ◽

Diverticular Disease ◽

State Of The Art ◽

Discriminatory Power ◽

Computed Tomographic ◽

Key Points

Purpose This overview sums up the Classification of Diverticular Disease (CDD) with regard to its application in computed tomographic diagnosis and briefly recapitulates its targeted advantages over preliminary systems. Primarily, application of the CDD in computed tomography diagnostics is described. Differences with respect to the categories of the older systems are pointed out on the level of each CDD type using imaging examples. Materials and Methods The presented images are derived from our institute according to the S2k criteria. Literature was researched on PubMed. Results The CDD constitutes an improvement compared to older systems for categorizing the stages of diverticular disease. It provides more discriminatory power on the descriptive-morphological level and defines as well as differentiates more courses of the disease. Furthermore, the categories translate more directly into state-of-the-art decision-making concerning hospitalization and therapy. Conclusion The CDD should be applied routinely in the computed tomographic diagnosis of diverticular disease. Typical imaging patterns are presented. Key points Citation Format

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A standardized, extensible framework for optimizing classification improves marker-gene taxonomic assignments

10.7287/peerj.preprints.934v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Nicholas A Bokulich ◽

Jai Ram Rideout ◽

Evguenia Kopylova ◽

Evan Bolyen ◽

Jessica Patnode ◽

...

Keyword(s):

Marker Gene ◽

Amplicon Sequencing ◽

Evaluation Framework ◽

Taxonomic Resolution ◽

Marker Genes ◽

Classification Methods ◽

New Methods ◽

Taxonomic Assignments ◽

Different Levels

Background: Taxonomic classification of marker-gene (i.e., amplicon) sequences represents an important step for molecular identification of microorganisms. Results: We present three advances in our ability to assign and interpret taxonomic classifications of short marker gene sequences: two new methods for taxonomy assignment, which reduce runtime up to two-fold and achieve high precision genus-level assignments; an evaluation of classification methods that highlights differences in performance with different marker genes and at different levels of taxonomic resolution; and an extensible framework for evaluating and optimizing new classification methods, which we hope will serve as a model for standardized and reproducible bioinformatics methods evaluations. Conclusions: Our new methods are accessible in QIIME 1.9.0, and our evaluation framework will support ongoing optimization of classification methods to complement rapidly evolving short-amplicon sequencing and bioinformatics technologies. Static versions of all of the analysis notebooks generated with this framework, which contain all code and analysis results, can be viewed at http://bit.ly/srta-010.

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Optimizing taxonomic classification of marker gene amplicon sequences

10.7287/peerj.preprints.3208v2 ◽

2018 ◽

Cited By ~ 4

Author(s):

Nicholas A Bokulich ◽

Benjamin D Kaehler ◽

Jai Ram Rideout ◽

Matthew Dillon ◽

Evan Bolyen ◽

...

Keyword(s):

Machine Learning ◽

Sequence Data ◽

Marker Gene ◽

Parameter Tuning ◽

Operating Conditions ◽

Evaluation Framework ◽

Taxonomic Classification ◽

Consensus Methods ◽

Learning Classifier

Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed the accuracy of existing methods for marker-gene amplicon sequence classification. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods of VSEARCH, BLAST+, and SortMeRNA) for classification of marker-gene amplicon sequence data. Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

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Optimizing taxonomic classification of marker gene sequences

10.7287/peerj.preprints.3208v1 ◽

2017 ◽

Cited By ~ 4

Author(s):

Nicholas A Bokulich ◽

Benjamin D Kaehler ◽

Jai Ram Rideout ◽

Matthew Dillon ◽

Evan Bolyen ◽

...

Keyword(s):

Machine Learning ◽

Marker Gene ◽

Parameter Tuning ◽

Operating Conditions ◽

Evaluation Framework ◽

Taxonomic Classification ◽

Gene Sequences ◽

Learning Classifier ◽

Classifier Performance

Background. Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results. We present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed classification accuracy of existing methods. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, BLAST+, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and VSEARCH and SortMeRNA alignment-based methods). Conclusions. Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make explicit recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

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Superscan: Supervised Single-Cell Annotation

10.1101/2021.05.20.445014 ◽

2021 ◽

Author(s):

Carolyn Shasha ◽

Yuan Tian ◽

Florian Mair ◽

Helen E Rodgers Miller ◽

Raphael Gottardo

Keyword(s):

Single Cell ◽

State Of The Art ◽

Marker Gene ◽

Surface Protein ◽

Cell Types ◽

Training Data ◽

Supervised Machine Learning ◽

Cell Type ◽

Surface Protein Expression ◽

Meta Analyses

Automated cell type annotation of single-cell RNA-seq data has the potential to significantly improve and streamline single cell data analysis, facilitating comparisons and meta-analyses. However, many of the current state-of-the-art techniques suffer from limitations, such as reliance on a single reference dataset or marker gene set, or excessive run times for large datasets. Acquiring high-quality labeled data to use as a reference can be challenging. With CITE-seq, surface protein expression of cells can be directly measured in addition to the RNA expression, facilitating cell type annotation. Here, we compiled and annotated a collection of 16 publicly available CITE-seq datasets. This data was then used as training data to develop Superscan, a supervised machine learning-based prediction model. Using our 16 reference datasets, we benchmarked Superscan and showed that it performs better in terms of both accuracy and speed when compared to other state-of-the-art cell annotation methods. Superscan is pre-trained on a collection of primarily PBMC immune datasets; however, additional data and cell types can be easily added to the training data for further improvement. Finally, we used Superscan to reanalyze a previously published dataset, demonstrating its applicability even when the dataset includes cell types that are missing from the training set.

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Deep learning models for bacteria taxonomic classification of metagenomic data

BMC Bioinformatics ◽

10.1186/s12859-018-2182-6 ◽

2018 ◽

Vol 19 (S7) ◽

Cited By ~ 29

Author(s):

Antonino Fiannaca ◽

Laura La Paglia ◽

Massimo La Rosa ◽

Giosue’ Lo Bosco ◽

Giovanni Renda ◽

...

Keyword(s):

Deep Learning ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Learning Models

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Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

Microbiome ◽

10.1186/s40168-018-0470-z ◽

2018 ◽

Vol 6 (1) ◽

Cited By ~ 513

Author(s):

Nicholas A. Bokulich ◽

Benjamin D. Kaehler ◽

Jai Ram Rideout ◽

Matthew Dillon ◽

Evan Bolyen ◽

...

Keyword(s):

Marker Gene ◽

Taxonomic Classification

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Re-purposing software for functional characterization of the microbiome

Microbiome ◽

10.1186/s40168-020-00971-1 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Laura-Jayne Gardiner ◽

Niina Haiminen ◽

Filippo Utro ◽

Laxmi Parida ◽

Ed Seabolt ◽

...

Keyword(s):

Functional Characterization ◽

Resource Development ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Molecular Function ◽

Metagenomic Sequencing ◽

Functional Classification ◽

Functional Hierarchy ◽

Speed Accuracy

Abstract Background Widespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit. Results To facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights. Conclusions We demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development.

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