Signature, a web server for taxonomic characterization of sequence samples using signature genes

Signature genes are genes that are unique to a taxonomic clade and are common within it. They contain a wealth of information about clade-specific processes and hold a strong evolutionary signal that can be used to phylogenetically characterize a set of sequences, such as a metagenomics sample. As signature genes are based on gene content, they provide a means to assess the taxonomic origin of a sequence sample that is complementary to sequence-based analyses. Here, we introduce Signature (http://www.cmbi.ru.nl/signature), a web server that identifies the signature genes in a set of query sequences, and therewith phylogenetically characterizes it. The server produces a list of taxonomic clades that share signature genes with the set of query sequences, along with an insightful image of the tree of life, in which the clades are color coded based on the number of signature genes present. This allows the user to quickly see from which part(s) of the taxonomy the query sequences likely originate

The PhyloPythiaS Web Server for Taxonomic Assignment of Metagenome Sequences

Metagenome sequencing is becoming common and there is an increasing need for easily accessible tools for data analysis. An essential step is the taxonomic classification of sequence fragments. We describe a web server for the taxonomic assignment of metagenome sequences with PhyloPythiaS. PhyloPythiaS is a fast and accurate sequence composition-based classifier that utilizes the hierarchical relationships between clades. Taxonomic assignments with the web server can be made with a generic model, or with sample-specific models that users can specify and create. Several interactive visualization modes and multiple download formats allow quick and convenient analysis and downstream processing of taxonomic assignments. Here, we demonstrate usage of our web server by taxonomic assignment of metagenome samples from an acidophilic biofilm community of an acid mine and of a microbial community from cow rumen

Mixture models for analysis of the taxonomic composition of metagenomes

Motivation: Inferring the taxonomic profile of a microbial community from a large collection of anonymous DNA sequencing reads is a challenging task in metagenomics. Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmentary sequences to phylogenetic categories, the accuracy of results largely depends on fragment length. This dependence complicates comparative analysis of data originating from different sequencing platforms or resulting from different preprocessing pipelines

Statistical methods for analyzing sequencing data with applications in modern biomedical analysis and personalized medicine

There has been tremendous advancement in sequencing technologies; the rate at which sequencing data can be generated has increased multifold while the cost of sequencing continues on a downward descent. Sequencing data provide novel insights into the ecological environment of microbes as well as human health and disease status but challenge investigators with a variety of computational issues. This thesis focuses on three common problems in the analysis of high-throughput data. The goals of the first project are to (1) develop a statistical framework and a complete software pipeline for metagenomics that identifies microbes to the strain level and thus facilitating a personalized drug treatment targeting the strain; and (2) estimate the relative content of microbes in a sample as accurately and as quickly as possible. The second project focuses on the analysis of the microbiome variation across multiple samples. Studying the variation of microbiomes under different conditions within an organism or environment is the key to diagnosing diseases and providing personalized treatments. The goals are to (1) identify various statistical diversity measures; (2) develop confidence regions for the relative abundance estimates; (3) perform multi-dimensional and differential expression analysis; and (4) develop a complete pipeline for multi-sample microbiome analysis. The third project is focused on batch effect analysis. When analyzing high dimensional data, non-biological experimental variation or “batch effects” confound the true associations between the conditions of interest and the outcome variable. Batch effects exist even after normalization. Hence, unless the batch effects are identified and corrected, any attempts for downstream analyses, will likely be error prone and may lead to false positive results. The goals are to (1) analyze the effect of correlation of the batch adjusted data and develop new techniques to account for correlation in two step hypothesis testing approach; (2) develop a software pipeline to identify whether batch effects are present in the data and adjust for batch effects in a suitable way. In summary, we developed software pipelines called PathoScope, PathoStat and BatchQC as part of these projects and validated our techniques using simulation and real data sets

PhiSiGns: an online tool to identify signature genes in phages and design PCR primers for examining phage diversity

<p>Abstract</p> <p>Background</p> <p>Phages (viruses that infect bacteria) have gained significant attention because of their abundance, diversity and important ecological roles. However, the lack of a universal gene shared by all phages presents a challenge for phage identification and characterization, especially in environmental samples where it is difficult to culture phage-host systems. Homologous conserved genes (or "signature genes") present in groups of closely-related phages can be used to explore phage diversity and define evolutionary relationships amongst these phages. Bioinformatic approaches are needed to identify candidate signature genes and design PCR primers to amplify those genes from environmental samples; however, there is currently no existing computational tool that biologists can use for this purpose.</p> <p>Results</p> <p>Here we present PhiSiGns, a web-based and standalone application that performs a pairwise comparison of each gene present in user-selected phage genomes, identifies signature genes, generates alignments of these genes, and designs potential PCR primer pairs. PhiSiGns is available at (<url>http://www.phantome.org/phisigns/</url>; <url>http://phisigns.sourceforge.net/</url>) with a link to the source code. Here we describe the specifications of PhiSiGns and demonstrate its application with a case study.</p> <p>Conclusions</p> <p>PhiSiGns provides phage biologists with a user-friendly tool to identify signature genes and design PCR primers to amplify related genes from uncultured phages in environmental samples. This bioinformatics tool will facilitate the development of novel signature genes for use as molecular markers in studies of phage diversity, phylogeny, and evolution.</p

Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

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

HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences

<p>Abstract</p> <p>Background</p> <p>One of the primary goals of comparative metagenomic projects is to study the differences in the microbial communities residing in diverse environments. Besides providing valuable insights into the inherent structure of the microbial populations, these studies have potential applications in several important areas of medical research like disease diagnostics, detection of pathogenic contamination and identification of hitherto unknown pathogens. Here we present a novel and rapid, alignment-free method called HabiSign, which utilizes patterns of tetra-nucleotide usage in microbial genomes to bring out the differences in the composition of both diverse and related microbial communities.</p> <p>Results</p> <p>Validation results show that the metagenomic signatures obtained using the HabiSign method are able to accurately cluster metagenomes at biome, phenotypic and species levels, as compared to an average tetranucleotide frequency based approach and the recently published dinucleotide relative abundance based approach. More importantly, the method is able to identify subsets of sequences that are specific to a particular habitat. Apart from this, being alignment-free, the method can rapidly compare and group multiple metagenomic data sets in a short span of time.</p> <p>Conclusions</p> <p>The proposed method is expected to have immense applicability in diverse areas of metagenomic research ranging from disease diagnostics and pathogen detection to bio-prospecting. A web-server for the HabiSign algorithm is available at <url>http://metagenomics.atc.tcs.com/HabiSign/</url>.</p

Exploring Viral Diversity in a Unique South African Soil Habitat

Abstract The Kogelberg Biosphere Reserve in the Cape Floral Kingdom in South Africa is known for its unique plant biodiversity. The potential presence of unique microbial and viral biodiversity associated with this unique plant biodiversity led us to explore the fynbos soil using metaviromic techniques. In this study, metaviromes of a soil community from the Kogelberg Biosphere Reserve has been characterised in detail for the first time. Metaviromic DNA was recovered from soil and sequenced by Next Generation Sequencing. The MetaVir, MG-RAST and VIROME bioinformatics pipelines were used to analyse taxonomic composition, phylogenetic and functional assessments of the sequences. Taxonomic composition revealed members of the order Caudovirales, in particular the family Siphoviridae, as prevalent in the soil samples and other compared viromes. Functional analysis and other metaviromes showed a relatively high frequency of phage-related and structural proteins. Phylogenetic analysis of PolB, PolB2, terL and T7gp17 genes indicated that many viral sequences are closely related to the order Caudovirales, while the remainder were distinct from known isolates. The use of single virome which only includes double stranded DNA viruses limits this study. Novel phage sequences were detected, presenting an opportunity for future studies aimed at targeting novel genetic resources for applied biotechnology