Machine Learning Solutions to Translational Genomics

Next Generation Sequencing Workflows with Advanced Feature Identification, State of art Analytics and Comprehensive annotation.

We have developed comprehensive Workflows for Various Applications of Next Generation Sequencing. Our workflows ensure you get most from your Genomics data incorporating state of art machine learning algorithms. Our workflows incorporate advanced feature extraction algorithm and extended annotation for Human and Mouse and incorporate pathways, ontologies and networks from wide range of database. These workflows can be executed inhouse or on our pre-configured servers and even on Cloud. Our expertise is on RNA-Sequencing, DNA-Sequencing, miRNA Sequencing, Exome Sequencing, Single Cell Sequencing, ChIP Sequencing, Strand Specific Sequencing and many more.

SEQOME-DL: Artificial Intelligence Deep Learning Platform for Development of Prognostic Model

SEQOME-DL is a Deep Learning Framework for Developing Predictive models. The platform has been successfully applied to Genomics, Healthcare, Drug Discovery and has potential to be used for a wide range of applications. It uses state of art technologies for Feature identification, Optimization of learning parameters, Network pruning and Cross validation. The framework is equipped with APIs to seamlessly integrate with existing IT infrastructure, Health Informatics systems, Diagnostics Lab and a wide range of existing systems.

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.

Cancer Omics to Network

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

Free Bioinformatics Consultancy

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

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


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