Machine Learning Solutions to Translational Genomics

Cloud Computing for Translational Genomics Research

SEQOME Workbench is our Cloud based Big Data Bioinformatics Analytic platform optimised for Next Generation Sequencing and Simulations.

SEQOME Workbench is designed for Next Generation Sequencing Applications, Simulations and other high throughput and computational intensive analytics. The inbuilt analytics is powerful enough to take control of the every step of analysis; run time decide the computational resources required and allocate what and when is needed.The Analytic Platform has APIs to integrates with Public and In-House Database and fully Integrates with the Analysis workflows and capable to Generating report, Send progress status and Reminders besides taking care of proper archival and disposal of intermediate files. SEQOME Analytic platform is designed to work on a High End Server, Cloud or Cluster of computers.

Predictome Workbench for Diagnostics and Clinical Outcomes

Prediction of Clinical outcomes to development of Novel Diagnostics techniques depends on how we layer the knowledge base with advanced algorithms. The current trend involves information from OMICS technologies involving multiple genes, proteins and metabolites to develop prediction algorithms with high accuracy and low false positives. It is becoming incredibly common in Cancer where it is becoming harder to predict the clinical outcomes or develop a diagnostic test which can accurately predict the best therapeutic option for the patients.

We combine our expertise in Knowledge Mining, Working with Public repositories and dataset generated by labs and use the information to develop advanced algorithms such as Artificial Intelligence, Experts Systems, Support Vector machines and Principal Component Analysis to accurately predict the clinical outcomes; or the results from Omics Based Diagnostic technology.

The whole analytics gets layered on our Cloud based Predictome Workbench which can be integrated with Laboratory Information Management System and Hospital Management System.

Artificial Intelligence can Predict Cancer Subtypes, Clinical Outcomes, Best Therapeutic Options and can be used for developing OMICS based Diagnostics.

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We have developed State of Art Cancer Specific Networks to support our Translational Genomics Pipeline

Our Network maps > 14,000 Genes mapped to > 40 Cancer Specific Gene Function. Our Drug-Gene Network includes 250 Cancer Drugs and 474 Genes and associated Pathways. This integrates with our High Throughput Translational Genomics pipeline and provides enhanced analytics and insight to Biological Systems. Our network driven approach is designed to help scientist and clinical research companies better interpretability of the Omics data generated from Next Generation Sequencing, Microarrays and Mass Spectrometry.

Bioinformatics Consultancy

Omics studies can be incredibly expensive. Technologies such as Next Generation Sequencing can be a huge burden to the project. More over in our experience 70% or the high throughput experiments had the scope for further improvements. In technology like Next Generation Sequencing, apart from the routine experimental design issues, multiple factors interplay in the process e.g. the questions asked and the sequencing depth and accordingly level of multiplexing. Proper planning of the experiment will not only save on the cost of experiment, but will also provide cleaner and more reliable results. We provide our clients with expert advice on planning an optimum high throughput experiment and also help writing grants for relevant sections. We also provide Bioinformatics data analysis support and training to our clients. Read More

Learn from the Experts: We organize Biostatistics and Bioinformatics workshop for Lab Scientist

Did you ever thought; why you are using p-values, should you be using 2 tailed test or a single tail test; is your experiment paired or not. Moreover, did you ever thought, if your data is normally distributed or not; is there any need of data transformation. No, then probably you would be missing on many significant results. You may be worried about high standard deviation to be depicted in figures. Could those error bars be a bit low? Under what condition should you use Standard Deviation, Standard Error or Coefficient of variation? Our Biostatistics and Bioinformatics workshop designed specifically for the Lab scientist will help you understand your data, help you not miss on the significant results and will empower you towards better depiction of results. We also organize workshop aiming at Omics Experimental Design, Data Analysis fundamentals and proper interpretation of results.

From our BLOGS

What all to look for in your RNA Sequencing Data

RNA Sequencing is a treasure-chest of information and quiet often we miss on potential ground breaking information in the RNA-SEQ datasets. This article will focus on conventional applications of RNA Sequencing, and will explore mining information for cSNP, Insertions Deletions & Fusion Genes, Alternate Splicing, Novel Genes/Exon, eQTL, and more. Read the BLOG

Working with Multi-Omics Dataset

Lab Scientist can easily be drained out interpreting results from a single set of OMICS data. Imagine the complexity working with Multiple and Varied OMICS experiment. This article focuses on methods on working with multi-omics data-set. Read the BLOG