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With the advent of new sequencing technologies continuously churning out increased number of publicly accessible genomic sequences there is a need to expedite the processing to achieve faster and reliable research objectives. The extent to which new genomic data is becoming available far exceeds the rate at which analysis and interpretation can be carried out. Examining the wealth of information contained within genomic sequences presents numerous additional computational challenges. Right from structure and function prediction to target identification to molecular modeling to lead identification and optimization demands competitive computational solutions.
By virtue of past experience OPTRA SYSTEMS has a lucid understanding of the challenges faced at all levels by genomics core lab and genome sequence center principal investigators, informaticians, research scientists, and lab techs.
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Genomics laboratory data management solution: |
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Sequencing service management with assays and library management, data analysis protocols and systems integration for management of operations and scientific data, from sample submission to results reporting.
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Genomics research application development: |
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Provide researcher friendly genomics applications or web portals for data mining, annotation, comparison and mapping across novel platforms in an interoperable manner through 3rd party interfacing. Developmental landscape tools by extracting the main features of multiple developmental time courses and project gene expression from a variety of sources onto this landscape. Solutions for Principal Component Analysis and query signature mapping developed on a principle to predict gene expression from multiple fully sequenced bacterial genomes, clustering based on BLAST scores, splitting and merging clusters into sub clusters based on phylogenetic divergence and domain architecture.
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Computational epigenomics offerings: |
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Development of prediction algorithms, quantitative modeling of epigenetic mechanism, Promoter prediction and CpG island prediction tools, DNA methylation analysis, prediction of nucleosome positioning, Epigenetic cancer biomarkers analysis, Integration of epigenome data with gene expression profiles, Visualization tools for whole genome analysis
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Plug in computational tools: |
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Gene prediction, Interaction networks, Pattern recognition, Exon and ORF prediction, Copy number variation analysis, Alternative splicing analysis and Promoter and transcription regulation analysis and Visualization tool for whole genome analysis.
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Genomics algorithm development: |
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Genomics algorithm development Sequence analysis, Polymorphism / SNP-data analysis, Phylogenetic and phylogenomics analysis, similarity search and sequence alignment, motif and domain scan, topology prediction and statistical interpretation |
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