Biomedical Knowledgebase Development (BKD):
Although there is an intense activity towards identifying novel biomarkers for cancers, especially those for early detection, it is yet not very clear whether there are already too many of the biomarkers described in the literature. Given that there is no central repository of data pertaining to any cancer, it is difficult to estimate many proteins described as “potential biomarkers” for any cancer.
An effective Graphical User Interface with N-Tier Architecture technology and utilization of open source tools in core to build a repository, will allow research in a much faster pace. A central repository will not only integrate all the information scattered across the literature, but will also serve as a reference for prioritizing and systematic testing of candidate biomarkers. MBiome BKD team develops databases as per Web 2.0 standards, which are more compatible with android systems.
Biomedical Literature Curation:
NCBI-Pubmed’s unprecedented information is hidden in the form of electronic papers. Our literature curation team curates information by reading through these papers and our database builts are enriched by our development team.
Genetics and Genomics:
Completion of the Human Genome Project and the recent 1000 genome projects have explored several insights into DNA constitution and phenotypic relation with respect to population. The development of an open source project, Bioconductor has eased the process of handling data by piping data to various algorithms developed for a particular data platform. It has been already proven in scientific community, the effective utilization of packages developed through Bioconductor to analyze data generated by sequencing technologies, genome wide association studies (GWAS), Gene expression studies etc. MBiome members have a good enough experience in handling genetic and genomics data with the effective usage of statistical methods.
The completion of the sequencing of human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to analysis and archive of proteomic data. Proteomic high throughput techniques like mass spectrometry, yeast two-hybrid, protein/peptide microarray, and fluorescence microscopy yield multidimensional datasets. Our proteomic group is highly competitive enough to work on large data sets using Mascot or X!Tandem suites.
The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods to deal with both the amount and the diversity of the data relating to the rich world of metabolites. Together with the other more established omics technologies, metabolomics will strengthen detailed understanding of the in vivo function of gene products, biochemical analysis, regulatory networks and more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Our metabolomic group has rich experience in exploring metabolite high throughput data using ‘known and unknown metabolite search’ approach and also relating such information in the context of pathways.
High performance computing:
The advent of high throughput technology in the field of molecular biology has enabled a comprehensive way of understanding molecular mechanisms associated with any biological process and reduced time factor involved in unraveling such complex mechanisms.
As these technologies are often used in the context of studying whole genome/proteome/metabolome in consideration, tailor-made accessories specially designed to conduct such experimental studies, are available. On the other hand, repetitive experiments using such accessories to obtain more precise answers, have increased computational challenges by generating unprecedented information with size ranging from few MB to 1 GB in each experiment. Such data are further validated with computers using specially developed statistical methods/algorithms and essentially requires a good hardware facility. But for many users, the available main memory - mostly 2 or 4 GB at a workstation level, limits the number of data files that may be analyzed. Hence, actual research and computations are limited by the available computer hardware. Using the potential of parallel computing and connecting several computers in grid fashion can be used to increase memory and the speed of execution of data analysis.
Considering this fact, MBiome has developed through MPI technology and have analyzed copious amount of data using such technologies. MBiome also utilizes Amazon EC2 cloud computing facility on rental basis when ever required.