Visual exploration of microbiome data

A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improvedunderstanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has beendeveloped to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content,and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendlyvisualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patternscorresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance inrecent times pertaining to the huge volume of the generated microbiome data. In this review, we present a brief overview ofsome useful visualization techniques that have significantly enriched microbiome data analytics.

Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls

Recent studies have highlighted the potential of ‘translational’ microbiome research in addressing real-world challengespertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positivelyimpacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation ofvarious ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic andprotein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challengefor computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target fordeveloping various translational applications. The challenge of deciphering signatures from an information-rich resourcelike the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome-based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with theimportance of a robust study design and the diligence required during every stage of analysis for achieving solutions withpotential translational value.

In silico dissection of Type VII Secretion System components across bacteria: New directions towards functional characterization.

Type VII Secretion System (T7SS) is one of the factors involved in virulence of Mycobacteriun tuberculosis H37Rv. Numerous research efforts have been made in the last decade towards characterizing the components of this secretion system. An extensive genome-wide analysis through compilation of isolated information is required to obtain a global view of diverse characteristics and pathogenicity-related aspects of this machinery. The present study suggests that differences in structural components (of T7SS) between Actinobacteria and Firmicutes, observed earlier in a few organisms, is indeed a global trend. A few hitherto uncharacterized T7SS-like clusters have been identified in the pathogenic bacteria Enterococcus faecalis, Saccharomonospora viridis, Streptococcus equi, Streptococcuss gordonii and Streptococcus sanguinis. Experimental verification of these clusters can shed lights on their role in bacterial pathogenesis. Similarly, verification of the identified variants of T7SS clusters consisting additional membrane components may help in unraveling new mechanism of protein translocation through T7SS. A database of various components of T7SS has been developed to facilitate easy access and interpretation of T7SS related data.

FASTR: A novel data format for concomitant representation of RNA sequence and secondary structure information.

Given the importance of RNA secondary structures in defining their biological role, it would be convenient for researchers seeking RNA data if both sequence and structural information pertaining to RNA molecules are made available together. Current nucleotide data repositories archive only RNA sequence data. Furthermore, storage formats which can frugally represent RNA sequence as well as structure data in a single file, are currently unavailable. This article proposes a novel storage format, ‘FASTR’, for concomitant representation of RNA sequence and structure. The storage efficiency of the proposed FASTR format has been evaluated using RNA data from various microorganisms. Results indicate that the size of FASTR formatted files (containing both RNA sequence as well as structure information) are equivalent to that of FASTA-format files, which contain only RNA sequence information. RNA secondary structure is typically represented using a combination of a string of nucleotide characters along with the corresponding dot-bracket notation indicating structural attributes. ‘FASTR’ – the novel storage format proposed in the present study enables a frugal representation of both RNA sequence and structural information in the form of a single string. In spite of having a relatively smaller storage footprint, the resultant ‘fastr’ string(s) retain all sequence as well as secondary structural information that could be stored using a dot-bracket notation. An implementation of the ‘FASTR’ methodology is available for download at http://metagenomics.atc.tcs.com/compression/fastr.

BIND – An algorithm for loss-less compression of nucleotide sequence data.

Recent advances in DNA sequencing technologies have enabled the current generation of life science researchers to probe deeper into the genomic blueprint. The amount of data generated by these technologies has been increasing exponentially since the last decade. Storage, archival and dissemination of such huge data sets require efficient solutions, both from the hardware as well as software perspective. The present paper describes BIND – an algorithm specialized for compressing nucleotide sequence data. By adopting a unique ‘block-length’ encoding for representing binary data (as a key step), BIND achieves significant compression gains as compared to the widely used general purpose compression algorithms (gzip, bzip2 and lzma). Moreover, in contrast to implementations of existing specialized genomic compression approaches, the implementation of BIND is enabled to handle non-ATGC and lowercase characters. This makes BIND a loss-less compression approach that is suitable for practical use. More importantly, validation results of BIND (with real-world data sets) indicate reasonable speeds of compression and decompression that can be achieved with minimal processor/ memory usage. BIND is available for download at http://metagenomics.atc.tcs.com/compression/BIND. No license is required for academic or non-profit use.

Eu-Detect: An algorithm for detecting eukaryotic sequences in metagenomic data sets.

Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences inmetagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells. The latter scenario usually occurs in the case of host-associatedmetagenomes. Identification and removal of contaminating sequences is important, since these sequences not only impact estimates of microbial diversity but also affect the accuracy of several downstream analyses. Currently, the computational techniques used for identifying contaminating eukaryotic sequences, being alignment based, are slow, inefficient, and require huge computing resources. In this article, we present Eu-Detect, an alignment-free algorithm that can rapidly identify eukaryotic sequences contaminating metagenomic data sets. Validation results indicate that on a desktop with modest hardware specifications, the Eu-Detect algorithm is able to rapidly segregate DNA sequence fragments of prokaryotic and eukaryotic origin, with high sensitivity. A Web server for the Eu-Detect algorithm is available at http://metagenomics.atc.tcs.com/Eu-Detect/.

INDeGenIUS, a new method for high-throughput identifi cation of specialized functional islands in completely sequenced organisms.

Genomic islands (GIs) are regions in the genome which are believed to have been acquired via horizontal gene transfer events and are thus likely to be compositionally distinct from the rest of the genome. Majority of the genes located in a GI encode a particular function. Depending on the genes they encode, GIs can be classifi ed into various categories, such as ‘metabolic islands’, ‘symbiotic islands’, ‘resistance islands’, ‘pathogenicity islands’, etc. The computational process for GI detection is known and many algorithms for the same are available. We present a new method termed as Improved N-mer based Detection of Genomic Islands Using Sequence-clustering (INDeGenIUS) for the identifi cation of GIs. This method was applied to 400 completely sequenced species belonging to proteobacteria. Based on the genes encoded in the identifi ed GIs, the GIs were grouped into 6 categories: metabolic islands, symbiotic islands, resistance islands, secretion islands, pathogenicity islands and motility islands. Several new islands of interest which had previously been missed out by earlier algorithms were picked up as GIs by INDeGenIUS. The present algorithm has potential application in the identifi cation of functionally relevant GIs in the large number of genomes that are being sequenced. Investigation of the predicted GIs in pathogens may lead to identifi cation of potential drug/vaccine candidates.