Understanding the biology in next generation sequencing data requires a comprehensive and integrated approach and must be supplemented with highly-curated data content from multiple sources. Genomatix, a computational biology company with a 22-year history of developing tools and data content for understanding the molecular mechanisms of eukaryotic gene expression, provides an easy-to-use bioinformatics software platform for the detailed analysis of next generation sequencing data. This lecture and hands-on training workshop series will introduce students to two complete, automated workflows for the independent analysis of RNA-Seq and ChIP-Seq data using the Genomatix platform.
Students will additionally have the option of taking a follow-on course and will learn about the advanced applications of the Genomatix platform for the meta-analysis and integration of RNA-Seq and ChIP-Seq data, including the identification of transcription factor binding sites, gene regulatory cassettes and genome-wide analysis of genes sharing a common transcription factor binding site pattern.
This course is broken down into morning and afternoon sessions. NOTE: This is the first course in a two-part series. Morning registration and attendance is required to attend the afternoon session.
The morning session (9:00 am-12:00 pm) will focus on RNA-Seq and will cover the topics of: project creation and management on the Genomatix Genome Analyzer (GGA); uploading FASTQ files to the GGA and the generation/visualization of sequence statistics; mapping of RNA-Seq reads to a reference genome and the generation/visualization of mapping statistics; principle component analysis; differential expression analysis using DESEq2; pathway and network analysis of differentially-expressed genes.
The afternoon session (1:30 pm-4:30 pm) will focus on ChIP-Seq and will cover the topics of: peak calling algorithms (MACS, SICER, NGSAnalyzer); peak finding; read classification; peak classification; sequence extraction of called peaks; Transcription Factor Binding Site (TFBS) overrepresentation; annotation of called peaks: next neighbor analysis.