Dataset Information

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

ABSTRACT: In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.

SUBMITTER: Gautam A

PROVIDER: S-EPMC8862659 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

Gautam Anupam A Felderhoff Hendrik H Bağci Caner C Huson Daniel H DH

mSystems 20220222 1

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI- ...[more]

PMID: 35191776

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Project description:The concept of a core microbiome has been broadly used to refer to the consistent presence of a set of taxa across multiple samples within a given habitat. The assignment of taxa to core microbiomes can be performed by several methods based on the abundance and occupancy (i.e., detection across samples) of individual taxa. These approaches have led to methodological inconsistencies, with direct implications for ecological interpretation. Here, we reviewed a set of methods most commonly used to infer core microbiomes in divergent systems. We applied these methods using large data sets and analyzed simulations to determine their accuracy in core microbiome assignments. Our results show that core taxa assignments vary significantly across methods and data set types, with occupancy-based methods most accurately defining true core membership. We also found the ability of these methods to accurately capture core assignments to be contingent on the distribution of taxon abundance and occupancy in the data set. Finally, we provide specific recommendations for further studies using core taxa assignments and discuss the need for unifying methodical approaches toward data processing to advance ecological synthesis. IMPORTANCE Different methods are commonly used to assign core microbiome membership, leading to methodological inconsistencies across studies. In this study, we review a set of the most commonly used core microbiome assignment methods and compare their core assignments using both simulated and empirical data. We report inconsistent classifications from commonly applied core microbiome assignment methods. Furthermore, we demonstrate the implication that variable core assignments may have on downstream ecological interpretations. Although we still lack a standardized approach to core taxa assignments, our study provides a direction to properly test core assignment methods and offers advances in model parameterization and method choice across distinct data types.

Dataset Information

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

Publications

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

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