A Scalable and flexible framework for analyzing large scale genomic data – MedGenome Webinar Series

January 24, 2021

By Dr. Kushal Suryamohan, MedGenome Inc.

Technological advances in sequencing capabilities have rapidly accelerated our understanding of human health and disease. From the workhorse short-read Illumina sequencing data to the recent advent of third-generation sequencing instruments such as PacBio, Nanopore, that now enable single molecule sequencing, genomics and its applications has assumed a wider scope in recent times ranging from specialised studies such as transcriptomics, epigenomics, metagenomics to more specific application areas such as biomarker discovery, pharmacogenomics, disease diagnosis, identification of novel genes in disease mechanisms, drug repurposing and disease risk predictions.

Further, Innovations in micro fluidics now allow researchers to study biological processes at the level of single cells where researchers can obtain and interpret data which provides lots of insights into cellular heterogeneity, lineage tracing study, cell population dynamics and gene expression profiles of single cells with huge application possibilities.

Given the increasing amounts of data that is generated, analysis and interpretation of such complex data is paramount to building on our knowledge base. Moreover, it is imperative to have a scalable framework to analyze, store, collect and visualize data with reproducible results. Bioinformatics is the interdisciplinary field of science that is aimed at realizing this goal.

MedGenome has successfully setup a universal bioinformatics one-stop platform MAnGO – (MedGenome Analytics for Genomics Platform) with powerful algorithms and workflows that allow parallel analysis of 100s and 1000s of samples with data interpretation and visualization capabilities that are scalable and customized for specific research applications and queries.

Watch the webinar below to find out more


#Biomarker discovery, #Transcriptomics, #Epigenomics, #Metagenomics, #Gene expression, #Bioinformatics, #single molecule sequencing

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