Upcoming Workshop – Optimizing your use of RNA-Seq tech & data analysis – 101

rna-seq

Workshop overview

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.

The agenda:

  • 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
  • Bioinformatician

Workshop leaders

Shanrong ZhaoSenior Manager, Computational Biology and Bioinformatics, Pfizer Worldwide Research & Development, Pfizer
Alexander DobinComputational Science Manager, Cold Spring Harbour Laboratory
Baohong ZhangDirector of Quantitative Bioinformatics, Pfizer

(find out more…)

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