metaxa2: improved identification and taxonomic classification of small and large subunit rRNA 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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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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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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Assessing alignment-based taxonomic classification of ancient microbial DNA

10.7287/peerj.preprints.27166v1 ◽

2018 ◽

Author(s):

Raphael Eisenhofer ◽

Laura Susan Weyrich

Keyword(s):

Ancient Dna ◽

Dna Sequences ◽

Random Sequence ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Data Sets ◽

Protein Alignments ◽

Microbial Dna ◽

Dna Characteristics

The field of paleomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ~100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future paleomicrobiological studies.

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Assessing alignment-based taxonomic classification of ancient microbial DNA

10.7287/peerj.preprints.27166 ◽

2018 ◽

Author(s):

Raphael Eisenhofer ◽

Laura Susan Weyrich

Keyword(s):

Ancient Dna ◽

Dna Sequences ◽

Random Sequence ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Data Sets ◽

Protein Alignments ◽

Microbial Dna ◽

Dna Characteristics

The field of paleomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ~100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future paleomicrobiological studies.

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Assessing alignment-based taxonomic classification of ancient microbial DNA

PeerJ ◽

10.7717/peerj.6594 ◽

2019 ◽

Vol 7 ◽

pp. e6594 ◽

Cited By ~ 5

Author(s):

Raphael Eisenhofer ◽

Laura Susan Weyrich

Keyword(s):

Ancient Dna ◽

Dna Sequences ◽

Random Sequence ◽

Taxonomic Classification ◽

Metagenomic Data ◽

Data Sets ◽

Protein Alignments ◽

Microbial Dna ◽

Dna Characteristics

The field of palaeomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to short DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ∼100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future palaeomicrobiological studies.

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Sympodiomyces attinorum sp. nov., a yeast species associated with nests of the leaf-cutting ant Atta sexdens

INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY ◽

10.1099/ijs.0.63200-0 ◽

2004 ◽

Vol 54 (5) ◽

pp. 1891-1894 ◽

Cited By ~ 27

Author(s):

Solange C. Carreiro ◽

Fernando C. Pagnocca ◽

Maurício Bacci ◽

Marc-André Lachance ◽

Odair C. Bueno ◽

...

Keyword(s):

Yeast Species ◽

Novel Species ◽

Large Subunit ◽

Growth Characteristics ◽

Rrna Gene ◽

The Novel ◽

Atta Sexdens ◽

Leaf Cutting ◽

Large Subunit Rrna ◽

Waste Deposit

Four strains of a novel yeast species were isolated from laboratory nests of the leaf-cutting ant Atta sexdens in Brazil. Three strains were found in older sponges and one was in a waste deposit in the ant nests. Sequencing of the D1/D2 region of the large-subunit rRNA gene showed that the novel species, named Sympodiomyces attinorum sp. nov., is phylogenetically related to Sympodiomyces parvus. Unlike Sympodiomyces parvus, Sympodiomyces attinorum can ferment glucose, assimilate methyl α-d-glucoside, salicin and citrate, and grow at 37 °C, thus enabling these two species to be distinguished. Differentiation from other related species is possible on the basis of other growth characteristics. The type strain of Sympodiomyces attinorum is UNESP-S156T (=CBS 9734T=NRRL Y-27639T).

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