C-DAC: SECG - Bioinformatics

The advent of numerous genome-sequencing projects like the Human Genome Project have led to millions of sequence residues flooding into the genome sequence databases like GenBank and EMBL. This biological data is being analyzed to provide structural and functional information on unknown genes or proteins, reconstructing metabolic pathways for detecting drug targets and so on using various computational tools, comparative genomics methods and microarray data analysis. The Bioinformatics Team at C-DAC deals with the development, porting and optimization of codes on PARAM (a parallel supercomputer developed by C-DAC) in the above areas and for mining large genomic databases, large molecular dynamics simulations, comparative genomic studies and gene expression data analysis.

Molecular Modeling

Molecular Dynamics (MD) simulation incorporates a deterministic molecular

modeling method to derive sequential sets of atomic positions by solving the differential equations embodied in Newton's Law of Motion. Molecular modeling programs like AMBER, CHARMM and GROMACS are widely used to carry out MD simulations. Carrying out large realistic simulations on biomolecules necessitate the use of high performance computing machines. Codes like AMBER, CHARMM and GROMACS have been ported and optimized on PARAM Padma. Using such codes on the PARAM system, it is possible to carry out large simulations of biomolecules for structural studies.

Biomolecular Docking

Biomolecular interactions are the core of all regulatory and metabolic processes that together constitute the process of life. To enable computer aided analysis of these interactions as well as automated prediction of molecular interactions, biomolecular-docking codes like FTDock and DARWIN have been ported and optimized on the PARAM Padma. This has tremendous application in the rational drug design process.

Ab-initio Methods

Efforts are engaged in the area of Quantum Chemistry to obtain stable structures and partial charges for various modified nucleotides, modified amino acids or any other drug molecule used in classical molecular dynamics simulation. The study plays an important role in finding active sites of drugs, stable and alternative conformations of proteins, nucleic acids or drug molecule, and also assists in obtaining parameters for molecular mechanics type potential energy function. The study uses codes like MOPAC and NWChem, which have been ported on PARAM Padma.

Genome Sequence Analysis

Genome sequence analysis deals with a range of popular tools beginning with dynamic programming methods like Smith-Waterman and heuristic methods like BLAST and FASTA, to multiple sequence alignment tools like CLUSTAL. Popular sequence analysis codes like BLAST, FASTA, Smith-Waterman and CLUSTAL have been ported on the PARAM Padma. Such a high throughput environment can be useful for large comparative genomic studies and rapid drug target identification.

Tools to analyze and annotate genomic DNA sequences and model organisms are being used extensively to identify coding regions so as to deduce the structure of genes and the resulting proteins. Gene finders for eukaryotic genome like HMMgene and Genscan, and Glimmer for microbial gene prediction are the most widely used and have been ported on PARAM Padma.

Comparative Genomics

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Pathway analysis tools have been ported on PARAM 10000 to reconstruct metabolic pathways, derived from annotated genome sequence as well as biochemical and physiological information. In silico metabolic pathway reconstruction, metabolic pathway comparison, pathway based analysis of expression data, using software such as Pathway Tools and KEGG system, and metabolic pathway engineering are the major goals of porting the codes on PARAM. Efforts in this direction can help in the validation of functional annotation, identification of novel pathways, identification of probable drug targets and metabolic pathway engineering for better processes.

Microarray Data Analysis

Microarray, a high-throughput expression technique monitors and analyzes gene expression profiles of thousands of genes simultaneously, and finds great significance in novel gene identification, disease diagnosis, drug discovery and toxicogenomics. MEME (Multiple EM (Expectation Maximization) for Motif Elicitation) is one of the tools available to detect motifs in a set of DNA or protein sequences. The parallel version of MEME has been ported on PARAM Padma while clustering tools like Genesis are being used to cluster the gene expression data of the genes with a similar motif in their upstream regions.

Problem Solving Environment (PSE)

PSE or Problem Solving Environment is a software that enables the use of high performance computing resources by providing users with a complete, integrated environment for a specific application. Its main advantage is that it makes available advanced hardware resources, software tools and assistance in a friendly environment that allows the user to concentrate more on the domain research problems. Other relevant features of the PSEs, which is built, using the J2EE three tier architecture, include multiple session handling, persistence state and visualization capabilities. At present, PSE's for molecular modeling codes like AMBER and CHARMM, and sequence analysis codes like Smith-Waterman, FASTA and BLAST have been developed for PARAM.

C-DAC is collaborating with the Indian Institute of Chemical Technology (IICT), Hyderabad to jointly offer training programme covering an Advanced Course in Bioinformatics. The course has been awarded recognition by the Jawaharlal Nehru Technical University (JNTU), Hyderabad. Under the training programme, students will have access to C-DAC's state-of-the-art PARAM supercomputers.