RNA-Seq Data Analysis in R (2-days online workshop)

The course is recommended for researchers who are familiar with programming in R or have completed our introductory course in R. The workshop will start with a presentation on the specificity of high-throughput data analysis such as RNA-Seq data. We will introduce you to the comprehensive analysis of the transcriptome such as quality control, mapping, initial filtration and normalization or higher-order analysis, i.e. differential analysis and gene ontology. We teach practical knowledge supported by examples, exercises and the so-called case study. The goal of the course is to introduce the essentials of next generation sequencing (NGS) data analysis. The classes are in the form of a workshop, with participants’ progress checking and the possibility of asking questions. After the end of the workshop, participants will receive a certificate confirming their attendance in the course.

Date: to be agreed with the client (2-days online workshop).

Time: 10 hours (5 hours each day with a coffee break).

Location: online.

Group: for a group larger than 3 persons. .

Teacher: Arkadiusz Kajdasz, Ph.D.

Price: 445 EUR (plus VAT if applicable).

Topics:

  • Introduction to RNA-Seq and Bioconductor
  • Analysis of sequencing quality
  • Mapping and reads counting
  • Pre-processing of counts
  • Essential statistical tests for NGS data: edgeR, DEseq, limma
  • GO and KEGG analysis
  • Results visualization
  • Case study

Learning Outcomes

After the course, the participant will be able to:

  1. Design a basic RNA-Seq experiment
  2. Understand the structure of files required for RNA-Seq analysis
  3. Know the RNA-Seq data processing protocol:
    1. Perform and interpret quality analysis of fastq files
    2. Remove adapter sequences from raw reads
    3. Align raw reads to a reference sequence
    4. Conduct mapping quality analysis
    5. Count reads mapping to genomic features
  4. Conduct differential gene expression analysis using the edgeR package
  5. Interpret the results of differential gene expression analysis with edgeR
  6. Determine the biological significance of differentially expressed genes (GO and KEGG analyses)
  7. Visualize the results of the analyses
  8. Know the RNA-Seq analysis tools available in Bioconductor
  9. Search databases for available RNA-Seq experiments

Arkadiusz Kajdasz, Ph.D.

Assistant professor at the Institute of Bioorganic Chemistry of the Polish Academy of Sciences, Poznań. He gained teaching experience working with students at the Adam Mickiewicz University, Poznań. Interested in the metabolism and post-transcriptional modifications of RNA. Practitioner in the analysis of RNA-Seq data. He participated in many bioinformatics courses organized in Poland and abroad (e.g., in Ontario Institute for Cancer Research, Toronto, Canada). He collaborates with the Poznań University of Life Sciences and the Military Institute of Medicine in Warsaw.

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