View the recorded presentation by Dr. Sandeep Sanga to learn more about using IPA for in silico RNA-Seq analysis; in this case, exploring mechanisms, biomarkers, and therapeutic targets for prostate adenocarcinoma.
Dr. Sanga is the Bioinformatics Product Development Scientist at Ingenuity Systems. His presentation at X-Gen explored how IPA® can be used with next generation sequencing (NGS) data to gain insights into the mechanisms, putative biomarkers, and therapeutic targets for prostate adenocarcinoma. The case study leverages NGS data through in silico RNA-Seq analysis and interpretation using CLC bio’s Genomics Workbench and IPA. The latest release of IPA includes support for RNA-Seq data.
Talk Abstract: Prostate adenocarcinoma is the most frequent carcinoma in men and the second leading cause of death in the male population worldwide. The goal of this study was to get novel insights into the mechanisms of the disease by leveraging the rapidly growing NGS data, and in particular, human transcriptome data through in silico data analysis and interpretation. The analysis of altered expression of genes and regulatory regions can pinpoint specific pathways and processes activated in growing cancer cells within tumors. Determining these activated pathways and networks can shed light on dysregulated processes, inform treatment options and highlight potential biomarkers with the ultimate goal to improve patient prognosis and treatment. High-resolution technologies, such as RNA-Seq, generate data that can be used to interrogate patient samples for expression changes and their patterns. Using short read RNA-Seq data from the NCBI SRA (Short Read Archive) public repository, gene expression changes from human prostate tumor and matched normal patient samples were assessed using CLC Genomics Workbench and CLC Genomics Server. To elucidate the underlying dysregulated biological processes, in silico pathway and mechanistic analysis was conducted in IPA by leveraging manually-curated biological information, canonical pathways and a variety of analytical tools. This presentation highlights some of the results of this integrated in silico analysis and introduces a proposed workflow for the analysis and interpretation of RNA-Seq data.
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