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

PrerequisitesUpdated

PLEASE NOTE that until further notice, due to the evolving situation with Coronavirus no courses will be offered as classroom based at the Training Facility. The Bioinformatics Team be teaching the course live online, with tutors available to help you work through the course material on a personal copy of the course environment. We will be aiming to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

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.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that these courses are only free for registered University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
• Further details regarding eligibility criteria are available here

• Basic experience of command line UNIX
• An introduction to the the Unix system and shell is available here.
• Basic knowledge of the R syntax
• For a beginner's introduction into R see here.

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 properly 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
• 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
• £ 50/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.
• £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 100/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

3

2 times a year

• Analysis of single cell RNA-seq data (IN-PERSON)
• Analysis of DNA Methylation using Sequencing (IN-PERSON)
• Introduction to R for Biologists (IN-PERSON)
• Extracting biological information from gene lists (IN-PERSON)