Suparna Mitra

Publications of Suparna Mitra

• Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN.

  Authors: Daniel H Huson, Suparna Mitra

  Methods in molecular biology (Clifton, N.J.). 01/2012; 856:415-29.

  Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Similarly, in metatranscriptomics and metaproteomics, the RNA and protein sequences ofMetagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Similarly, in metatranscriptomics and metaproteomics, the RNA and protein sequences of such samples are studied. The analysis of these kinds of data often starts by asking the questions of "who is out there?", "what are they doing?", and "how do they compare?". In this chapter, we describe how these computational questions can be addressed using MEGAN, the MEtaGenome ANalyzer program. We first show how to analyze the taxonomic and functional content of a single dataset and then show how such analyses can be performed in a comparative fashion. We demonstrate how to compare different datasets using ecological indices and other distance measures. The discussion is conducted using a number of published marine datasets comprising metagenomic, metatranscriptomic, metaproteomic, and 16S rRNA data.

• Analysis of 16S rRNA environmental sequences using MEGAN.

  Authors: Suparna Mitra, Mario Stärk, Daniel H Huson

  BMC genomics. 11/2011; 12 Suppl 3:S17.

  Metagenomics 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 ofMetagenomics 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. We 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. Environmental 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.

• Integrative analysis of environmental sequences using MEGAN4.

  Authors: Daniel H Huson, Suparna Mitra, Hans-Joachim Ruscheweyh, Nico Weber, Stephan C Schuster

  Genome research. 06/2011; 21(9):1552-60.

  A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic andA major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

• Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

  Authors: Suparna Mitra, Paul Rupek, Daniel Richter, Tim Urich, Jack Gilbert, Folker Meyer, Andreas Wilke, Daniel Huson

  BMC Bioinformatics. 01/2011;

  Abstract Background Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods areAbstract Background Metagenomics 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. Results The 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. Conclusions The 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.

• Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG.

  Authors: Suparna Mitra, Paul Rupek, Daniel C Richter, Tim Urich, Jack A Gilbert, Folker Meyer, Andreas Wilke, Daniel H Huson

  BMC bioinformatics. 01/2011; 12 Suppl 1:S21.

  Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increaseMetagenomics 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. The 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. The 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.

• Comparison of multiple metagenomes using phylogenetic networks based on ecological indices.

  Authors: Suparna Mitra, Jack A Gilbert, Dawn Field, Daniel H Huson

  The ISME journal. 10/2010; 4(10):1236-42.

  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 theSecond-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.

• Short clones or long clones? A simulation study on the use of paired reads in metagenomics.

  Authors: Suparna Mitra, Max Schubach, Daniel H Huson

  BMC bioinformatics. 01/2010; 11 Suppl 1:S12.

  Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. MostMetagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies. This paper addresses the problem of taxonomical analysis of paired reads. We describe a new feature of our metagenome analysis software MEGAN that allows one to process sequencing reads in pairs and makes assignments of such reads based on the combined bit scores of their matches to reference sequences. Using this new software in a simulation study, we investigate the use of Illumina paired-sequencing in taxonomical analysis and compare the performance of single reads, short clones and long clones. In addition, we also compare against simulated Roche-454 sequencing runs. This work shows that paired reads perform better than single reads, as expected, but also, perhaps slightly less obviously, that long clones allow more specific assignments than short ones. A new version of the program MEGAN that explicitly takes paired reads into account is available from our website.

• Visual and Statistical Comparison of Metagenomes.

  Authors: Suparna Mitra, Bernhard Klar, Daniel H Huson

  Bioinformatics (Oxford, England). 07/2009;

  BACKGROUND: Metagenomics is the study of the genomic content of an environmental sample of microbes. Advances in the throughput and cost-efficiency of sequencing technology is fueling a rapidBACKGROUND: Metagenomics is the study of the genomic content of an environmental sample of microbes. Advances in the throughput 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 paper 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 stand-alone 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 ideaspresented in this paper, can be obtained from our website at:www-ab.informatik.uni-tuebingen.de/software/megan. CONTACT: mitra@informatik.uni-tuebingen.de.

• Methods for comparative metagenomics.

  Authors: Daniel H Huson, Daniel C Richter, Suparna Mitra, Alexander F Auch, Stephan C Schuster

  BMC bioinformatics. 02/2009; 10 Suppl 1:S12.

  BACKGROUND: Metagenomics 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, inBACKGROUND: Metagenomics 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. RESULTS: This 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. CONCLUSION: There 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.