RNA-seq is becoming increasingly popular and widely used in transcriptome profiling. Current RNA-seq approaches use shotgun sequencing technologies such as Illumina, in which millions or even billions of short reads are generated from a randomly fragmented cDNA library. For most RNA-seq studies, the data analysis involves the following key steps:
- Raw sequence data QC
- Mapping reads to reference genome or transcriptome
- Counting mapped reads to individual genes or transcripts
- Differential analysis to identify significant gene between different biological conditions
Despite the fact that a large number of algorithms have been developed for RNA-seq data analyses in recent years, there are still many open questions for accurate read mapping, gene quantification and data normalization. In this workshop, we’ll cover a few practical questions pertinent on large-scale RNA-seq data analyses.
What you can expect:
To get a deep understanding of those practical challenges in RNA-seq data analyses, including measure of gene quantification, choice of gene annotation, multiple mapped reads, data integration, and visualization and sharing; and help them to perform better RNA-seq data analyses.
- What is the right measure for gene expression: RPKM, TPM, CPM or else?
- Why the choice of a gene model is important for gene quantification?
- How to effectively share your results with bench scientists?
- Why we need to switch from non-stranded to stranded RNA-seq?
- What is the best way to deal with multiple mapping reads?
- How to integrate RNA-seq data of similar researches from different labs and service providers?
This workshop is for:
- RNA-seq data analysts
Shanrong Zhao, Senior Manager, Computational Biology and Bioinformatics, Pfizer Worldwide Research & Development, Pfizer
Alexander Dobin, Computational Science Manager, Cold Spring Harbour Laboratory
Baohong Zhang, Director of Quantitative Bioinformatics, Pfizer