Dataset Information

Metabolic classification of microbial genomes using functional probes.

ABSTRACT:

Background

Microorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which focus on the evolutionary relationships of organisms, classifications of the genomes based on alternative points of view may help advance our understanding of the delicate relationships of organisms.

Results

We have developed a proteome-based method for classifying microbial species. This classification method uses a set of probes comprising short, highly conserved amino acid sequences. For each genome, in silico translation is performed to obtained its proteome, based on which a probe-set frequency pattern is generated. Then, the probe-set frequency patterns are used to cluster the proteomes/genomes.

Conclusions

Features of the proposed method include a high running speed in challenge of a large number of genomes, and high applicability for classifying organisms with incomplete genome sequences. Moreover, the probe-set clustering method is sensitive to the metabolic phenotypic similarities/differences among species and is thus supposed potential for the classification or differentiation of closely-related organisms.

SUBMITTER: Lee CC

PROVIDER: S-EPMC3355368 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolic classification of microbial genomes using functional probes.

Lee Chi-Ching CC Lo Wei-Cheng WC Lai Szu-Ming SM Chen Yi-Ping Phoebe YP Tang Chuan Yi CY Lyu Ping-Chiang PC

BMC genomics 20120427

<h4>Background</h4>Microorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which fo ...[more]

PMID: 22537274

Similar Datasets

Project description:BackgroundAdvances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.ResultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric "MW-score" (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.ConclusionMETABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC . Video abstract.

Project description:BackgroundHigh-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user.ResultsHere, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator .ConclusionsWe demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots.

Dataset Information

Metabolic classification of microbial genomes using functional probes.

Background

Results

Conclusions

Publications

Metabolic classification of microbial genomes using functional probes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets