In genomics and computational biology. there is too often a divide between those who generate the data and those who analyze it. A physician scientist treating ovarian cancer patients will approach the analysis of ovarian cancer gene expression data with a very different mindset than a computational biologist or biostatistician and, if able to follow his instincts through hands-on analysis, is very likely to reach different insights that if working through a quantitative scientist as an intermediary.
The Center for Cancer Computational Biology has developed WebMeV (https://mev.tm4.org/), an open-source, web-based application that takes advantage of cloud computing resources. WebMeV is designed to provide users with access to cutting edge genomic analysis tools for RNASeq analysis. With intuitive user interfaces and data visualization to analyze large RNASeq data, users are able to load their private data or directly access large public domain data sets, such as GEO and TCGA, through the platform.
Don’t miss the upcoming Webinar – Fri, April 7, 2017 – 10:00 AM – 11:00 AM EDT
WEBINAR: INTRODUCTION TO PUBLIC RNASEQ DATA ANALYSIS USING WEBMEV
In this tutorial, we will demonstrate the followings:
- How to upload private data and perform RNASeq analysis on WebMeV
- How to pull level 3 TCGA data and use clinical data to stratify the cohort
- How to perform complex set operation to create trait specific cohorts
- Use WebMeV to perform RNASeq analysis and explore high dimensional data
- Use WebMeV to perform unsupervised RNASeq analysis
- How to pull GEO data sets directly from GEO