Analysis of Adaptive Immune Receptor Repertoires

using high throughput sequencing data (NGS)

Downloadable poster in PDF

   IMPORTANT DATES for this Course
   Deadline for applications: Oct 12th 2016
   Course dates: Nov 2nd - Nov 4th 2016

Candidates with adequate profile will be accepted in the next 72 hours after the application until we reach 20 participants.


Simon D. W. Frost is a computational biologist based at the University of Cambridge, who has worked on the dynamics and evolution of viruses for over twenty years. He has also developed biostatistics and bioinformatics courses, most recently focusing on the use of R.
Detailed information: Simon Frost
Affiliation: University of Cambridge, Cambridge, UK

Course Description

This course will cover the analysis of B and T cell repertoires (adaptive immune receptor repertoires, or AIRR) from high-throughput sequencing data using bioinformatics workflows. Topics include germline allele assignment, identification of clones, and visualisation/analysis of clonal frequencies. The course will also cover emerging standards (VDJML, Change-O) in the analysis and reporting of Adaptive-immune Receptor Repertoire (AIRR) data. In addition to an introduction to software used, such as IgBLAST, pRESTO, Change-O, etc., the course will also provide an introduction to the use of Jupyter notebooks for conducting exploratory analyses and the Common Workflow Language to automate analyses.

To get a flavour of the techniques and software that will be used in the course.
Discussion site: B-T.CR

Target audience

The course was designed for experimentalist and students who want to explore adaptive immune repertoires using NGS data.
Individuals that are working - or looking forward to work - in the area of T cell or B cell receptor analysis will benefit from attending this training course.


Basic knowledge of the process of T and B cell receptor generation is essential.
Knowledge and data handling of file formats used in next generation sequencing analysis (FASTQ, SAM/BAM) highly desirable.
Familiarity with use of the Unix/Linux command line.
Basic knowledge of Python desirable, but not essential.

Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:  Aug 22nd 2016