Tungadri Bose

DELIMINATE—a fast and efficient method for loss-less compression of genomic sequences

SUMMARY An unprecedented quantity of genome sequence data is currently being generated using next-generation sequencing platforms. This has necessitated the development of novel bioinformatics approaches and algorithms that not only facilitate a meaningful analysis of these data but also aid in efficient compression, storage, retrieval and transmission of huge volumes of the generated data. We present a novel compression algorithm (DELIMINATE) that can rapidly compress genomic sequence data in a loss-less fashion. Validation results indicate relatively higher compression efficiency of DELIMINATE when compared with popular general purpose compression algorithms, namely, gzip, bzip2 and lzma. AVAILABILITY AND IMPLEMENTATION Linux, Windows and Mac implementations (both 32 and 64-bit) of DELIMINATE are freely available for download at: http://metagenomics.atc.tcs.com/compression/DELIMINATE. CONTACT sharmila@atc.tcs.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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.

FQC: A novel approach for efficient compression, archival, and dissemination of fastq datasets

Sequence data repositories archive and disseminate fastq data in compressed format. In spite of having relatively lower compression efficiency, data repositories continue to prefer GZIP over available specialized fastq compression algorithms. Ease of deployment, high processing speed and portability are the reasons for this preference. This study presents FQC, a fastq compression method that, in addition to providing significantly higher compression gains over GZIP, incorporates features necessary for universal adoption by data repositories/end-users. This study also proposes a novel archival strategy which allows sequence repositories to simultaneously store and disseminate lossless as well as (multiple) lossy variants of fastq files, without necessitating any additional storage requirements. For academic users, Linux, Windows, and Mac implementations (both 32 and 64-bit) of FQC are freely available for download at: https://metagenomics.atc.tcs.com/compression/FQC .

Computational Analysis of Host–Pathogen Protein Interactions between Humans and Different Strains of Enterohemorrhagic Escherichia coli

Serotype O157:H7, an enterohemorrhagic Escherichia coli (EHEC), is known to cause gastrointestinal and systemic illnesses ranging from diarrhea and hemorrhagic colitis to potentially fatal hemolytic uremic syndrome. Specific genetic factors like ompA, nsrR, and LEE genes are known to play roles in EHEC pathogenesis. However, these factors are not specific to EHEC and their presence in several non-pathogenic strains indicates that additional factors are involved in pathogenicity. We propose a comprehensive effort to screen for such potential genetic elements, through investigation of biomolecular interactions between E. coli and their host. In this work, an in silico investigation of the protein–protein interactions (PPIs) between human cells and four EHEC strains (viz., EDL933, Sakai, EC4115, and TW14359) was performed in order to understand the virulence and host-colonization strategies of these strains. Potential host–pathogen interactions (HPIs) between human cells and the “non-pathogenic” E. coli strain MG1655 were also probed to evaluate whether and how the variations in the genomes could translate into altered virulence and host-colonization capabilities of the studied bacterial strains. Results indicate that a small subset of HPIs are unique to the studied pathogens and can be implicated in virulence. This subset of interactions involved E. coli proteins like YhdW, ChuT, EivG, and HlyA. These proteins have previously been reported to be involved in bacterial virulence. In addition, clear differences in lineage and clade-specific HPI profiles could be identified. Furthermore, available gene expression profiles of the HPI-proteins were utilized to estimate the proportion of proteins which may be involved in interactions. We hypothesized that a cumulative score of the ratios of bound:unbound proteins (involved in HPIs) would indicate the extent of colonization. Thus, we designed the Host Colonization Index (HCI) measure to determine the host colonization potential of the E. coli strains. Pathogenic strains of E. coli were observed to have higher HCIs as compared to a non-pathogenic laboratory strain. However, no significant differences among the HCIs of the two pathogenic groups were observed. Overall, our findings are expected to provide additional insights into EHEC pathogenesis and are likely to aid in designing alternate preventive and therapeutic strategies.

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.

Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

BackgroundMycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment.ResultsThe newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen.ConclusionsThe comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.