CHO Consortium

Consortium for Chinese Hamster Ovary Cell Systems Biotechnology

Workshops on Genomic Tools

The consortium organizes workshops periodically in Minnesota or in a location convenient for members on topics related to systems biotechnology and on mastering tools for the analysis of genomic and transcriptomic data. Some workshops focus on concepts and principles, others involve hands-on use of software and sample data analysis. Some topics have been repeated. The consortium also compiles and integrates software pipelines to streamline data analysis and makes the integrated algorithms available to members.

Principles and concepts of transcriptome analysis

  • Microarray data analysis: data normalization, transcriptome statistics, multiple-hypothesis testing, and time-series data analysis.
  • Mining high-dimensional transcriptome data: pattern recognition, unsupervised and supervised classification methods (e.g. clustering, support vector machine, etc.)
  • Pathway, network and functional class analysis using Ingenuity Pathway Analysis (IPA), gene ontology (GO) browser and web based “Database for Annotation, Visualization and Integrated Discovery (DAVID)”.
  • Case studies: illustration of transcriptome analysis
    • Time-series data analysis for dynamic promoter identification.
    • Meta-analysis of transcriptome data for hyper productivity gene-set establishment.
    • Transcriptome variability in clones and sub-clones.

Hands-on session on transcriptomic and genomic analysis

  • Microarray data analysis using R:

We established an automated pipeline for microarray data analysis and this workshop is an exercise of using that pipeline. The pipeline was implemented in an R script, which performs raw data processing, data normalization, probe annotation, statistical analysis of differentially expressed genes (SAM).

  • RNA-seq mapping and data analysis: overview of mapping and software required, data processing and quality control, mapping to reference genome or transcriptome, pile-up, RPKM/FPKM calculation, differential expression analysis and visualization.
  • Transgene integration site analysis: method design and data analysis of transgene integration sites.
  • IGV genome browser: RNA-seq data visualization with annotation tracks.

Hands-on analysis of consortium custom-designed transcriptome microarray

  • Transcriptome analysis: practical analysis for discovery, functional analysis (pathway visualization and analysis using R package Pathview, or GSEA and DAVID),

In this workshop, we introduced R portable, a pre-installed version of R with RStudio. R portable is ready to use and does not require any further installation because it already contains all the required R packages for microarray data processing and differential analysis.

Note: R portable is for use as a stand-alone software package. R packages can be sensitive to versions of R. Sometimes, users cannot install different packages properly if the version is incorrect. Using R portable avoids that problem. However, it may run into problems when installing other R packages into R portable.

  • Survey of pathways, and discussion on attainable or unattainable goals of transcriptome analysis.
  • Meta-analysis of transcriptome data (batch correction), how to use transcriptome archived data.

Comparative Genomic Hybridization (CGH) and transcriptome analysis refresher

  • Principles and concepts of Comparative Genomic Hybridization (CGH), data normalization, probe annotation, data analysis and some examples.
  • Analysis of gain or loss of gene copies in stable and unstable cell lines