Analysis of bulk RNA-seq data (ONLINE LIVE TRAINING)

Prerequisites

The aim of this course is to familiarize the participants with the primary analysis of RNA-seq data.

This course starts with a brief introduction to RNA-seq and discusses quality control issues. Next, we will present the alignment step, quantification of expression and differential expression analysis. For downstream analysis we will focus on tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq.

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

• 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.

• 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).

Number of sessions: 3

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

After this course you should be able to:

• Design your RNA-Seq experiments properly, 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
• Produce a list of differentially expressed genes from an RNA-seq experiment

During this course you will learn about:

• RNA sequencing technology and considerations on experimental design
• Quality control of raw and aligned sequencing reads: FASTQC and Picard
• Read alignment to a reference genome: Hisat2
• Extract information from SAM/BAM files: samtools
• Sources of variation in RNA-seq data
• Differential expression analysis usingDEseq
• Annotation resources in Bioconductor
• Identifying over-represented gene sets among a list of differentially expressed genes

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)