Proteomics and genomics information has revolutionized how R&D and clinical decision support is being managed, but there is a huge bottleneck resulting in a slow rate of progress for these initiatives. Optra's advanced Bioinformatics service offerings help organizations to effortlessly overcome challenges like integrating and managing proteomics and genomic data from different resources, workflows or equipment, while transforming it into a homogeneous resource.
We develop algorithms and applications for mining and analysis of meaningful data to meet the needs of commercial genetic diagnostic labs and academic proteomics and genomics core facilities. Our LIMS solutions enable scientists and researchers to easily and efficiently collect, store, manage, retrieve and analyze the enormous amounts of data required for their genomic research.
We support a wide range of services catering to:
Some of Optra’s successful Bioinformatics projects are:
The Optra team has successfully supported Fortune 500 bio-pharma companies by delivering a customized, multi-disciplined approach to drug repositioning based on advanced computational algorithms.
This application helps the user to find homologous protein information, protein annotation, interacting proteins and molecules information, as well as predicted and known interactions. It also facilitates multiple sequence alignment, viewing phylogenetic trees, and SMILES structures of the molecules associated with the proteins. The data required is generated by using bacterial genome data and clustering proteins based on their homology.
Optra Health developed a comprehensive bioinformatics pipeline to process microarray data. This service involved analysis of bio-statistical concepts to associate statistical data output to biologically relevant targets. This helped to quickly narrow down the initial query list of differentially expressed genes to a few relevant genes for further validation and analysis. The output is a precise list of biologically relevant targets backed with reported functional data annotations and pathway information with interaction data (where applicable) that could be used for further validations, drug discovery and therapeutics research. Other analysis tools include transformation (Logarithmic, Inverse), data normalization (RMA, MAS, Lowess, Quantile), clustering (hierarchical, non-hierarchical), functional enrichment, pathway analysis, etc.
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