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MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.

ABSTRACT: Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species in environmental samples. In this study, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains). Different from all existing methods, MBMC bins reads by measuring the similarity of reads to the trained Markov chains for different taxa instead of directly comparing reads with known genomic sequences. By testing on more than 24 simulated and experimental datasets with species of similar abundance, species of low abundance, and/or unknown species, we report here that MBMC reliably grouped reads from different species into separate bins. Compared with four existing approaches, we demonstrated that the performance of MBMC was comparable with existing approaches when binning reads from sequenced species, and superior to existing approaches when binning reads from unknown species. MBMC is a pivotal tool for binning metagenomic reads in the current era of Big Data and postgenomic integrative biology. The MBMC software can be freely downloaded at http://hulab.ucf.edu/research/projects/metagenomics/MBMC.html .

SUBMITTER: Wang Y

PROVIDER: S-EPMC4982950 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.

Wang Ying Y Hu Haiyan H Li Xiaoman X

Omics : a journal of integrative biology 20160722 8

Metagenomics is a next-generation omics field currently impacting postgenomic life sciences and medicine. Binning metagenomic reads is essential for the understanding of microbial function, compositions, and interactions in given environments. Despite the existence of dozens of computational methods for metagenomic read binning, it is still very challenging to bin reads. This is especially true for reads from unknown species, from species with similar abundance, and/or from low-abundance species ...[more]

PMID: 27447888

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Project description:Metagenomic next-generation sequencing (mNGS) allows the monitoring of microbiota composition of murine colonies employed for scientific purposes in a single test by assessing the composition of gut microbiome and the detection of pathogens from fecal pellets. In this study, we tested the potential use of mNGS for monitoring both microbiota composition and the presence of pathogens through Environmental Health Monitoring, by using exhaust dust collection filters derived from individually ventilated cages (IVC) systems.mNGS analysis was performed on nucleic acids isolated from filters collecting air from the exhaust of: (1) cages with mice housed in a non-pathogen free facility; (2) animal-free cages with clean chow and bedding from the same facility; (3) cages housing mice from a specific-pathogen free (SPF) facility. mNGS results revealed correspondence between microbiome composition from fecal pellets and filter, including pathogenic bacteria (Helicobacter hepaticus, Helicobacter typhlonius, Chlamydia muridarum, Rodentibacter pneumotropicus, Citrobacter rodentium), intestinal protozoa (Tritrichomonas muris, Spironucleus muris) nematoda (Aspiculuris tetraptera) and eukaryotic parasites (Myocoptes musculinus), present in the colony. Entamoeba muris and Syphacia obvelata were detected in fecal pellets but not in filter. The animal free exhaust dust filter, exposed to clean cages (no mice) placed in the IVC after removal of all mice, exhibited the presence of the same pathogens due to contaminated connecting pipes, confirming the sensitivity of the approach. Conversely, the filter from SPF colony revealed the absence of pathogens.The current use of exhaust dust collection filters in health surveillance requires multiple molecular tests to identify specific pathogens and does not provide information on the colony microbiome. This work provides the proof-of-principle that assaying exhaust dust collection filters by mNGS for microbiota monitoring of laboratory mice is feasible. In its daily application, results suggest the usefulness of the test in SPF facilities, where pathogenic micro-organisms are expected to be absent. mNGS analysis of exhaust dust collection filters allows the analysis of multiple cages, reducing the number of tests required for pathogen detection and corresponding costs, and avoiding the use of sentinel mice.

Project description:BackgroundBiogas production is an economically attractive technology that has gained momentum worldwide over the past years. Biogas is produced by a biologically mediated process, widely known as "anaerobic digestion." This process is performed by a specialized and complex microbial community, in which different members have distinct roles in the establishment of a collective organization. Deciphering the complex microbial community engaged in this process is interesting both for unraveling the network of bacterial interactions and for applicability potential to the derived knowledge.ResultsIn this study, we dissect the bioma involved in anaerobic digestion by means of high throughput Illumina sequencing (~51 gigabases of sequence data), disclosing nearly one million genes and extracting 106 microbial genomes by a novel strategy combining two binning processes. Microbial phylogeny and putative taxonomy performed using >400 proteins revealed that the biogas community is a trove of new species. A new approach based on functional properties as per network representation was developed to assign roles to the microbial species. The organization of the anaerobic digestion microbiome is resembled by a funnel concept, in which the microbial consortium presents a progressive functional specialization while reaching the final step of the process (i.e., methanogenesis). Key microbial genomes encoding enzymes involved in specific metabolic pathways, such as carbohydrates utilization, fatty acids degradation, amino acids fermentation, and syntrophic acetate oxidation, were identified. Additionally, the analysis identified a new uncultured archaeon that was putatively related to Methanomassiliicoccales but surprisingly having a methylotrophic methanogenic pathway.ConclusionThis study is a pioneer research on the phylogenetic and functional characterization of the microbial community populating biogas reactors. By applying for the first time high-throughput sequencing and a novel binning strategy, the identified genes were anchored to single genomes providing a clear understanding of their metabolic pathways and highlighting their involvement in anaerobic digestion. The overall research established a reference catalog of biogas microbial genomes that will greatly simplify future genomic studies.

Dataset Information

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.

Publications

MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.

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