Analysis of bulk RNA-seq data (IN-PERSON)

Prerequisites

In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance, including for University of Cambridge students. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals

Essential

• Basic understanding of high-throughput sequencing technologies.
  • Watch this iBiology video for an excellent overview.
• A working knowledge of the UNIX command line (course registration page).
  • If you are not able to attend this prerequisite course, please work through our Unix command line materials ahead of the course (up to section 7).
• A working knowledge of R (course registration page).
  • If you are not able to attend this prerequisite course, please work through our R materials ahead of the course.

Desirable

• A working knowledge of running analysis on High Performance Computing (HPC) clusters (course registration page).

Bioinformatics, Functional genomics, Data visualisation, Transcriptomics, Data handing, Data mining, RNA-seq

During this course you will learn about:

• RNA sequencing technology and considerations on experimental design
• Quality control of raw sequencing reads using FASTQC
• Alignment and quantification of gene expression using Salmon
• Importing and doing exploratory analysis of RNA-seq data in R
• Statistical analysis of RNA-seq data
• Differential expression analysis using DEseq2
• Gene annotation resources in Bioconductor
• Identifying over-represented gene sets among a list of differentially expressed genes

After this course you should be able to:

• Properly design your RNA-Seq experiments, considering advantages and limitations of RNA-seq assays
• Assess the quality of your datasets
• Perform alignment and quantification of expression through different tools and pipelines
• Know what tools are available in Bioconductor for RNA-seq data analysis and understand the basic object types that are utilised
• Fit an adequate statistical model to your data and extract lists of differentially expressed genes for comparisons of interest
• Assign biological meaning to your gene lists
• Produce several visualisations to communicate your results

Presentations, demonstrations and practicals

• Free for registered University of Cambridge students
• £ 60/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 60/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 120/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

3

Several times per year

• Introduction to working with UNIX and bash (IN-PERSON)
• Introduction to R for Biologists (IN-PERSON)
• Analysis of single cell RNA-seq data (IN-PERSON)
• High Performance Computing: An Introduction (IN-PERSON)
• Analysis of DNA Methylation using Sequencing (IN-PERSON)