September 22, 2019  |  

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly.

The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step.Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools. BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.


September 22, 2019  |  

Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation.

Shotgun metagenomics methods enable characterization of microbial communities in human microbiome and environmental samples. Assembly of metagenome sequences does not output whole genomes, so computational binning methods have been developed to cluster sequences into genome ‘bins’. These methods exploit sequence composition, species abundance, or chromosome organization but cannot fully distinguish closely related species and strains. We present a binning method that incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing. Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins. We validate our method using synthetic and real microbiome sequences. In addition to genome binning, we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample. Incorporation of DNA methylation information into shotgun metagenomics analyses will complement existing methods to enable more accurate sequence binning.


September 22, 2019  |  

Analysis of gut microbiota – An ever changing landscape.

In the last two decades, the field of metagenomics has greatly expanded due to improvement in sequencing technologies allowing for a more comprehensive characterization of microbial communities. The use of these technologies has led to an unprecedented understanding of human, animal, and environmental microbiomes and have shown that the gut microbiota are comparable to an organ that is intrinsically linked with a variety of diseases. Characterization of microbial communities using next-generation sequencing-by-synthesis approaches have revealed important shifts in microbiota associated with debilitating diseases such as Clostridium difficile infection. But due to limitations in sequence read length, primer biases, and the quality of databases, genus- and species-level classification have been difficult. Third-generation technologies, such as Pacific Biosciences’ single molecule, real-time (SMRT) approach, allow for unbiased, more specific identification of species that are likely clinically relevant. Comparison of Illumina next-generation characterization and SMRT sequencing of samples from patients treated for C. difficile infection revealed similarities in community composition at the phylum and family levels, but SMRT sequencing further allowed for species-level characterization – permitting a better understanding of the microbial ecology of this disease. Thus, as sequencing technologies continue to advance, new species-level insights can be gained in the study of complex and clinically-relevant microbial communities.


September 22, 2019  |  

MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs.

There are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing, and there is a need to adapt short read tools to long read datasets.We describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods.All presented methods are implemented in the open source edition of MEGAN, and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community.This article was reviewed by Nicola Segata together with Moreno Zolfo, Pete James Lockhart and Serghei Mangul.This work extends the applicability of the widely-used metagenomic analysis software MEGAN to long reads. Our study suggests that the presented LAST+MEGAN-LR pipeline is sufficiently fast and accurate.


September 22, 2019  |  

Quantitative metaproteomics highlight the metabolic contributions of uncultured phylotypes in a thermophilic anaerobic digester.

In this study, we used multiple meta-omic approaches to characterize the microbial community and the active metabolic pathways of a stable industrial biogas reactor with food waste as the dominant feedstock, operating at thermophilic temperatures (60°C) and elevated levels of free ammonia (367 mg/liter NH3-N). The microbial community was strongly dominated (76% of all 16S rRNA amplicon sequences) by populations closely related to the proteolytic bacterium Coprothermobacter proteolyticus. Multiple Coprothermobacter-affiliated strains were detected, introducing an additional level of complexity seldom explored in biogas studies. Genome reconstructions provided metabolic insight into the microbes that performed biomass deconstruction and fermentation, including the deeply branching phyla Dictyoglomi and Planctomycetes and the candidate phylum “Atribacteria” These biomass degraders were complemented by a synergistic network of microorganisms that convert key fermentation intermediates (fatty acids) via syntrophic interactions with hydrogenotrophic methanogens to ultimately produce methane. Interpretation of the proteomics data also suggested activity of a Methanosaeta phylotype acclimatized to high ammonia levels. In particular, we report multiple novel phylotypes proposed as syntrophic acetate oxidizers, which also exert expression of enzymes needed for both the Wood-Ljungdahl pathway and ß-oxidation of fatty acids to acetyl coenzyme A. Such an arrangement differs from known syntrophic oxidizing bacteria and presents an interesting hypothesis for future studies. Collectively, these findings provide increased insight into active metabolic roles of uncultured phylotypes and presents new synergistic relationships, both of which may contribute to the stability of the biogas reactor.Biogas production through anaerobic digestion of organic waste provides an attractive source of renewable energy and a sustainable waste management strategy. A comprehensive understanding of the microbial community that drives anaerobic digesters is essential to ensure stable and efficient energy production. Here, we characterize the intricate microbial networks and metabolic pathways in a thermophilic biogas reactor. We discuss the impact of frequently encountered microbial populations as well as the metabolism of newly discovered novel phylotypes that seem to play distinct roles within key microbial stages of anaerobic digestion in this stable high-temperature system. In particular, we draft a metabolic scenario whereby multiple uncultured syntrophic acetate-oxidizing bacteria are capable of syntrophically oxidizing acetate as well as longer-chain fatty acids (via the ß-oxidation and Wood-Ljundahl pathways) to hydrogen and carbon dioxide, which methanogens subsequently convert to methane. Copyright © 2016 American Society for Microbiology.


September 22, 2019  |  

Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data.

DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomic projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample that averaged 1319 nt in length and 99.7% accuracy. CCS data assembly generated a comparative number of large contigs greater than 1?kb, to those assembled from a ~190x larger HiSeq dataset (~18 Gb) produced from the same sample (i.e approximately 62% of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.


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