Skip to content
Vidjil -- High-throughput Analysis of V(D)J Immune Repertoire (mirror, please go to
JavaScript C++ Python CSS HTML C Other
Branch: master
Clone or download
Latest commit 6c8d2a6 Mar 14, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
algo vidjil-algo: release 2019.03 Mar 14, 2019
browser Merge branch 'feature-sc/fix-external-tests-for-chrome' into 'dev' Mar 13, 2019
demo 1043-IGK.should-vdj.fa: New perspective Jul 12, 2018
doc vidjil-algo: release 2019.03 Mar 14, 2019
docker vidjil-server/Dockerfile: refer to server/Makefile to download web2py Feb 28, 2019
germline tests: check in/outframe genes Dec 6, 2018
packaging Add IgReC support Apr 13, 2018
reports Makefile: Clean Valgrind reports before launching Valgrind on all the… Mar 12, 2015
server update default config Mar 12, 2019
.gitignore .gitignore: update Dec 27, 2018
.landscape.yml .landscape.yml: ignoring 'Undefined name' from pyflakes Apr 24, 2015, converted and updated from *.org Jul 16, 2018
Makefile add make rules for headless_server Jul 17, 2018
mkdocs.yml doc/: détails, compression bandeau Oct 8, 2018

V(D)J recombinations in lymphocytes are essential for immunological diversity. They are also useful markers of pathologies, and in leukemia, are used to quantify the minimal residual disease during patient follow-up. High-throughput sequencing (NGS/HTS) now enables the deep sequencing of a lymphoid population with dedicated Rep-Seq methods and softwares.

The Vidjil platform contains three components. Vidjil-algo processes high-througput sequencing data to extract V(D)J junctions and gather them into clones. Vidjil-algo starts from a set of reads and detects "windows" overlapping the actual CDR3. This is based on an fast and reliable seed-based heuristic and allows to output all sequenced clones. The analysis is extremely fast because, in the first phase, no alignment is performed with database germline sequences.

The Vidjil web application is made for the interactive visualization and analysis of clones and their tracking along the time in a MRD setup or in a immunological study. The web application can visualize data processed by the Vidjil algorithm or by other V(D)J analysis pipelines, and enables to explore further cluterings proposed by software and/or done manually done by the user. The web application can be linked to a sample, experiment and patient database able to store sequencing data and metadata, to run RepSeq software and to save annotations directly from the web application, with authentication. Clinicians or researchers in immunology or hematology can manage, upload, analyze and annotate their runs directly on the web applicaiton.

Vidjil components


The web application

  • Access at (demo login:, password: vidjil)
  • Please contact us if you would like to test your data and have a full account on the web server
  • Development code is under browser/ and server/ (a make in those directories will get the necessary files)
  • Documentation is in doc/, it is also available from

Code and license

Vidjil is open-source, released under GNU GPLv3 license. You are welcome to redistribute it under certain conditions. This software is for research use only and comes with no warranty.

The development code is available on Bug reports, issues and patches are welcome.


We welcome Bitcoin donations to 13u12m6LxVhesKEpS6T5wpYN19LHpwk8xt. Thank you for your support !

The Vidjil team

Vidjil is developed by Aurélien Béliard, Mathieu Giraud, Ryan Herbert, Tatiana Rocher and Mikaël Salson from the Bonsai bioinformatics team (CRIStAL, CNRS, U. Lille, Inria Lille). Vidjil is also developed by external colleagues: Marc Duez located in Bristol (School of Social and Community Medicine, University of Bristol) and Florian Thonier located in Rennes (department of hematology) Vidjil is developed in collaboration with the department of Hematology of CHRU Lille, the Functional and Structural Genomic Platform (U. Lille 2, IFR-114, IRCL), and the EuroClonality-NGS working group. The research is supported by SIRIC ONCOLille (Grant INCa-DGOS-Inserm 6041), by Région Nord-Pas-de-Calais/Hauts-de-France (ABILES), by Inria and by InCA.


If you use Vidjil for your research, please cite the following references:

Marc Duez et al., “Vidjil: A web platform for analysis of high-throughput repertoire sequencing”, PLOS ONE 2016, 11(11):e0166126

Mathieu Giraud, Mikaël Salson, et al., “Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing”, BMC Genomics 2014, 15:409

You may also be interested in the following publication for the diagnosis of acute lymphoblastic leukemia with high-throughput sequencing:

Yann Ferret, Aurélie Caillault, et al., “Multi-loci diagnosis of acute lymphoblastic leukaemia with high-throughput sequencing and bioinformatics analysis”, British Journal of Haematology 2016

You can’t perform that action at this time.