Suparna Mitra

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce

Methods for comparative metagenomics

BackgroundMetagenomics is a rapidly growing field of research that aims at studying uncultured organisms to understand the true diversity of microbes, their functions, cooperation and evolution, in environments such as soil, water, ancient remains of animals, or the digestive system of animals and humans. The recent development of ultra-high throughput sequencing technologies, which do not require cloning or PCR amplification, and can produce huge numbers of DNA reads at an affordable cost, has boosted the number and scope of metagenomic sequencing projects. Increasingly, there is a need for new ways of comparing multiple metagenomics datasets, and for fast and user-friendly implementations of such approaches.ResultsThis paper introduces a number of new methods for interactively exploring, analyzing and comparing multiple metagenomic datasets, which will be made freely available in a new, comparative version 2.0 of the stand-alone metagenome analysis tool MEGAN.ConclusionThere is a great need for powerful and user-friendly tools for comparative analysis of metagenomic data and MEGAN 2.0 will help to fill this gap.

Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

BackgroundMetagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets.ResultsThe focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets.ConclusionsThe MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website.

Effects of Surgical and Dietary Weight Loss Therapy for Obesity on Gut Microbiota Composition and Nutrient Absorption

Evidence suggests a correlation between the gut microbiota composition and weight loss caused by caloric restriction. Laparoscopic sleeve gastrectomy (LSG), a surgical intervention for obesity, is classified as predominantly restrictive procedure. In this study we investigated functional weight loss mechanisms with regard to gut microbial changes and energy harvest induced by LSG and a very low calorie diet in ten obese subjects (n = 5 per group) demonstrating identical weight loss during a follow-up period of six months. For gut microbiome analysis next generation sequencing was performed and faeces were analyzed for targeted metabolomics. The energy-reabsorbing potential of the gut microbiota decreased following LSG, indicated by the Bacteroidetes/Firmicutes ratio, but increased during diet. Changes in butyrate-producing bacterial species were responsible for the Firmicutes changes in both groups. No alteration of faecal butyrate was observed, but the microbial capacity for butyrate fermentation decreased following LSG and increased following dietetic intervention. LSG resulted in enhanced faecal excretion of nonesterified fatty acids and bile acids. LSG, but not dietetic restriction, improved the obesity-associated gut microbiota composition towards a lean microbiome phenotype. Moreover, LSG increased malabsorption due to loss in energy-rich faecal substrates and impairment of bile acid circulation. This trial is registered with ClinicalTrials.gov NCT01344525.

Visual and statistical comparison of metagenomes

BACKGROUND Metagenomics is the study of the genomic content of an environmental sample of microbes. Advances in the through-put and cost-efficiency of sequencing technology is fueling a rapid increase in the number and size of metagenomic datasets being generated. Bioinformatics is faced with the problem of how to handle and analyze these datasets in an efficient and useful way. One goal of these metagenomic studies is to get a basic understanding of the microbial world both surrounding us and within us. One major challenge is how to compare multiple datasets. Furthermore, there is a need for bioinformatics tools that can process many large datasets and are easy to use. RESULTS This article describes two new and helpful techniques for comparing multiple metagenomic datasets. The first is a visualization technique for multiple datasets and the second is a new statistical method for highlighting the differences in a pairwise comparison. We have developed implementations of both methods that are suitable for very large datasets and provide these in Version 3 of our standalone metagenome analysis tool MEGAN. CONCLUSION These new methods are suitable for the visual comparison of many large metagenomes and the statistical comparison of two metagenomes at a time. Nevertheless, more work needs to be done to support the comparative analysis of multiple metagenome datasets. AVAILABILITY Version 3 of MEGAN, which implements all ideas presented in this article, can be obtained from our web site at: www-ab.informatik.uni-tuebingen.de/software/megan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Analysis of 16S rRNA environmental sequences using MEGAN

BackgroundMetagenomics is a rapidly growing field of research aimed at studying assemblages of uncultured organisms using various sequencing technologies, with the hope of understanding the true diversity of microbes, their functions, cooperation and evolution. There are two main approaches to metagenomics: amplicon sequencing, which involves PCR-targeted sequencing of a specific locus, often 16S rRNA, and random shotgun sequencing. Several tools or packages have been developed for analyzing communities using 16S rRNA sequences. Similarly, a number of tools exist for analyzing randomly sequenced DNA reads.ResultsWe describe an extension of the metagenome analysis tool MEGAN, which allows one to analyze 16S sequences. For the analysis all 16S sequences are blasted against the SILVA database. The result output is imported into MEGAN, using a synonym file that maps the SILVA accession numbers onto the NCBI taxonomy.ConclusionsEnvironmental samples are often studied using both targeted 16S rRNA sequencing and random shotgun sequencing. Hence tools are needed that allow one to analyze both types of data together, and one such tool is MEGAN. The ideas presented in this paper are implemented in MEGAN 4, which is available from: http://www-ab.informatik.uni-tuebingen.de/software/megan.

Comparison of multiple metagenomes using phylogenetic networks based on ecological indices

Second-generation sequencing technologies are fueling a vast increase in the number and scope of metagenome projects. There is a great need for the development of new methods for visualizing the relationships between multiple metagenomic data sets. To address this, a novel approach is presented that combines the use of taxonomic analysis, ecological indices and non-hierarchical clustering to provide a network representation of the relationships between different metagenome data sets. The approach is illustrated using several published data sets of different types, including metagenomes, metatranscriptomes and 16S ribosomal profiles. Application of the approach to the same data summarized at different taxonomical levels gives rise to remarkably similar networks, indicating that the analysis is very robust. Importantly, the networks provide the both visual definition and metric quantification for the non-rooted relationship between samples, combining the desirable characteristics of other tools into one.

Analysis of the intestinal microbiota using SOLiD 16S rRNA gene sequencing and SOLiD shotgun sequencing

BackgroundMetagenomics seeks to understand microbial communities and assemblages by DNA sequencing. Technological advances in next generation sequencing technologies are fuelling a rapid growth in the number and scope of projects aiming to analyze complex microbial environments such as marine, soil or the gut. Recent improvements in longer read lengths and paired-sequencing allow better resolution in profiling microbial communities. While both 454 sequencing and Illumina sequencing have been used in numerous metagenomic studies, SOLiD sequencing is not commonly used in this area, as it is believed to be more suitable in the context of reference-guided projects.ResultsTo investigate the performance of SOLiD sequencing in a metagenomic context, we compared taxonomic profiles of SOLiD mate-pair sequencing reads with Sanger paired reads and 454 single reads. All sequences were obtained from the bacterial 16S rRNA gene, which was amplified from microbial DNA extracted from a human fecal sample. Additionally, from the same fecal sample, complete genomic microbial DNA was extracted and shotgun sequenced using SOLiD sequencing to study the composition of the intestinal microbiota and the existing microbial metabolism. We found that the microbiota composition of 16S rRNA gene sequences obtained using Sanger, 454 and SOLiD sequencing provide results comparable to the result based on shotgun sequencing. Moreover, with SOLiD sequences we obtained more resolution down to the species level. In addition, the shotgun data allowed us to determine a functional profile using the databases SEED and KEGG.ConclusionsThis study shows that SOLiD mate-pair sequencing is a viable and cost-efficient option for analyzing a complex microbiome. To the best of our knowledge, this is the first time that SOLiD sequencing has been used in a human sample.

In silico analyses of metagenomes from human atherosclerotic plaque samples

BackgroundThrough several observational and mechanistic studies, microbial infection is known to promote cardiovascular disease. Direct infection of the vessel wall, along with the cardiovascular risk factors, is hypothesized to play a key role in the atherogenesis by promoting an inflammatory response leading to endothelial dysfunction and generating a proatherogenic and prothrombotic environment ultimately leading to clinical manifestations of cardiovascular disease, e.g., acute myocardial infarction or stroke. There are many reports of microbial DNA isolation and even a few studies of viable microbes isolated from human atherosclerotic vessels. However, high-resolution investigation of microbial infectious agents from human vessels that may contribute to atherosclerosis is very limited. In spite of the progress in recent sequencing technologies, analyzing host-associated metagenomes remain a challenge.ResultsTo investigate microbiome diversity within human atherosclerotic tissue samples, we employed high-throughput metagenomic analysis on: (1) atherosclerotic plaques obtained from a group of patients who underwent endarterectomy due to recent transient cerebral ischemia or stroke. (2) Presumed stabile atherosclerotic plaques obtained from autopsy from a control group of patients who all died from causes not related to cardiovascular disease. Our data provides evidence that suggest a wide range of microbial agents in atherosclerotic plaques, and an intriguing new observation that shows these microbiota displayed differences between symptomatic and asymptomatic plaques as judged from the taxonomic profiles in these two groups of patients. Additionally, functional annotations reveal significant differences in basic metabolic and disease pathway signatures between these groups.ConclusionsWe demonstrate the feasibility of novel high-resolution techniques aimed at identification and characterization of microbial genomes in human atherosclerotic tissue samples. Our analysis suggests that distinct groups of microbial agents might play different roles during the development of atherosclerotic plaques. These findings may serve as a reference point for future studies in this area of research.

Mining and assessment of catabolic pathways in the metagenome of a common effluent treatment plant to induce the degradative capacity of biomass

Metagenome analysis was used to understand the microbial community in activated sludge treating industrial wastewaters at a Common Effluent Treatment Plant (CETP) in South India. The taxonomic profile mapped onto National Center for Biotechnology Information (NCBI) taxonomy using MEtaGenome ANalyzer (MEGAN), demonstrated that the most abundant domain belonged to prokaryotes, dominated by bacteria. Bacteria representing nine phyla were identified from the sequence data including representatives from two new phyla, Synergistetes and Elusimicrobia. Functional analysis of the metagenome, with specific reference to the metabolism of aromatic compounds, revealed the dominance of genes of the central meta-cleavage pathway. This information was used to improve the degradative efficiency in the wastewater treatment plant. A pilot scale plant was set up with 200L of activated sludge using salicylate induced sludge and results demonstrated 52% removal in chemical oxygen demand (COD) against non-induced biomass.