Sharmila S Mande
Articles written in Journal of Biosciences
Volume 35 Issue 3 September 2010 pp 351-364 Articles
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 classified 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 identification of GIs. This method was applied to 400 completely sequenced species belonging to proteobacteria. Based on the genes encoded in the identified 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 identification of functionally relevant GIs in the large number of genomes that are being sequenced. Investigation of the predicted GIs in pathogens may lead to identification of potential drug/vaccine candidates.
Volume 36 Issue 4 September 2011 pp 709-717 Articles
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
Volume 37 Issue 4 September 2012 pp 785-789 Articles
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
Volume 40 Issue 3 September 2015 pp 571-577 Articles
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
Volume 41 Issue 1 March 2016 pp 133-143 Article
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.