Dr. Sandeep Sanga presented on using IPA® and Ingenuity® Variant Analysis™ for in silico RNA-Seq analysis 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 and Ingenuity Variant Analysis 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 walks through the analysis of RNA-Seq data from FASTQ files generated by the primary analysis of short reads coming off the sequencing machine through to patient-specific biological interpretation.
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 derive patient-specific insights into the mechanisms of the disease by leveraging paired tumor-normal human transcriptomic NGS data with a rapid, integrated, in silico data analysis workflow. The analysis of altered expression of genes, their isoforms, and regulatory regions can pinpoint specific pathways and processes activated or inhibited in growing cancer cells within tumors. Determining these activated and inhibited pathways, functions, processes, and transcriptional programs can shed light on important dysregulated mechanisms, 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, isoform expression changes and variants from human prostate tumor and matched normal patient samples were computed and assessed using multiple software tools including CLC Bio’s Genomic Workbench and Server, Bowtie/Bowtie2, Cufflinks, DESeq, GenePattern, Samtools, BEDtools, GATK, and R/Bioconductor. To elucidate the underlying dysregulated biological processes, analysis was conducted using new IPA features released in December 2011 by leveraging manually-curated biological information, causal analytics for predicting activated/inhibited biological functions and transcription factors, identification of isoform-specific disease markers, canonical pathways and a variety of other IPA features. To identify candidate genetic drivers and potential therapeutic markers, variants were analyzed using the recently launched Ingenuity Variant Analysis. This presentation highlights some of the results of this integrated, in silico analysis and introduces a proposed workflow for the rapid interpretation of RNA-Seq data.
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