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Vidjil

High-Throughput Analysis
of V(D)J Immune Repertoire

Upload

  • .fasta
  • .fastq
  • .gz
  • Patient database

Process

  • Ultra-fast algorithm
  • 1 Gbp in < 5 minutes
    for one locus
  • On app.vidjil.org or self-hosted

Analyze

  • Explore lymphocyte populations
  • Tag, annotate and merge clones
  • Send to IMGT/V-QUEST and to IgBlast
  • Generate a report

Vidjil is an open-source platform for the analysis of high-throughput sequencing data from lymphocytes, developed by the Bonsai bioinformatics lab and the VidjilNet consortium.

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 sequencing methods and software, called either Rep-Seq or AIRR-Seq.

Vidjil is used in routine clinical practice in hospitals around the world, in particular for the diagnosis of patients suffering Acute Lymphoblastic Leukemia (ALL). More than 8,000 samples of ALL patients were analyzed with Vidjil since 2015. Vidjil is also used in several studies on hemopathies (ALL, CLL, lymphomas, WM...) as well as on immunology topics involving T-cell or B-cell repertoires.


Repertoire analysis. Vidjil analyses the entire B/T immune repertoire. The Genescan-like view, here colored by locus, gives a first approach of the diversity of the sample and allows to assess the mono/poly-clonality.

Clone tracking. Vidjil allows to compare several samples and to track clones along the time. This ALL patient has a relapse at day 308 on (minor) clones that were present at the diagnosis and that expanded troughout time.

CLL, productivity analysis. For CLL as well as immunological analyses, Vidjil estimates the productivity of each clone. It can also display information computed by IMGT/V-QUEST, such as on hypermutations.

Intra-run contamination. The comparison between several samples can be used to assess the inter-sample contamination, notably when there is a control sample.

Spikes. Vidjil offer several normalization options, for example where there is a control spike with known abundance.


The Vidjil algorithm processes
     sequences recombined on the TRA/D, TRB, TRG, IGH, IGK and IGL locus,
     possibly with some incomplete/unusal recombinations such as Dh-Jh, Intron-KDE or Dd2-Dd3.

High-throughput algorithm

At the heart of the Vidjil platform, 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. It detects gene rearrangements from both immunoglobulins and T-cell receptors, as well as some incomplete or uncommon rearrangements. The analysis is based on a reliable seed-based algorithm. It is extremely fast because, in the first phase, no alignment is performed with database germline sequences. The algorithm works on reads coming from either amplicon-based or capture-based deep sequencing strategy, as soon as they include CDR3 sequences. Both human and mouse immune systems can be analyzed.


Several views of the Vidjil web application displaying TRG clones of a patient with ALL: grid, time graph and clone sequences

Multi-sample web application

The Vijdil web application is made for the visualization, inspection and analysis of clones and their tracking along the time in a MRD setup or in a immunological study. The application can visualize data processed by the Vidjil algorithm or by other V(D)J analysis pipelines. It enables to explore further clusterings proposed by the software and/or done manually done by the user.


The patient view of the Vidjil web application. The database enables to store information on both the patient and individual samples.

Patient/experiment and sample database

The web application can be linked to a patient/experiment and sample database. After authentication, the clinicians or researchers upload .fasta/.fastq/.gz files, manage and process their runs directly from the web application. They can save their analysis and generate reports for the patient record. The server with the sample database can be installed in any hospital or computer center. A public web server is also available since October 2014. More than 20 labs in Europe and in the world regularly use Vidjil through the web application. In 18 months, the public server analyzed more than 5,000,000 sequences from 5,000 AIRR/RepSeq samples.

Robust. Fast. Open-source.

Strong attention to users and stability. High-quality development process, with systemating testing and continous integration.

VidjilNet membership and donations

We welcome new members in the VidjilNet consortium. We also welcome Bitcoin donations to 13u12m6LxVhesKEpS6T5wpYN19LHpwk8xt. Thank you for your support !

Extended collaborations

All the code of Vidjil is available under open-source licenses (GPLv3, as well as some other free licences for some third-parties librairies). We also offer extended support as well as custom development for various types of projects. Please contact us if you are interested.

Credits

Vidjil is developed by a passionate team with fast response, from the VidjilNet consortium and the Bonsai bioinformatics team of the CRIStAL (CNRS, U. Lille) and Inria Lille research centers, in Lille, France. This work is 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, and is supported by SIRIC ONCOLille (Grant INCa-DGOS-Inserm 6041), Région Nord-Pas-de-Calais (ABILES), Université Lille (the scientific computing service, Mésocentre de Lille for hosting our servers, as well as the former PPF bioinformatique), and Inria Lille. The methods were presented at the JOBIM 2013 and ASH 2014 conferences, and are described in the following papers:

Some publications using Vidjil

Chrystelle Abdo et al., Caution encouraged in next-generation sequencing immunogenetic analyses in acute lymphoblastic leukemia Blood, 2020, 136(9):1105–1107 https://doi.org/10.1182/blood.2020005613

Jean-Sebastien Allain et al., IGHV segment utilization in immunoglobulin gene rearrangement differentiates patients with anti-myelin-associated glycoprotein neuropathy from others immunoglobulin M-gammopathies, Haematologica, 2018, 103:e207-e210 http://dx.doi.org/10.3324/haematol.2017.177444

Jack Bartram et al., High throughput sequencing in acute lymphoblastic leukemia reveals clonal architecture of central nervous system and bone marrow compartments Haematologica, 2018, https://dx.doi.org/10.3324%2Fhaematol.2017.174987

Sébastien Bender et al., Immunoglobulin variable domain high-throughput sequencing reveals specific novel mutational patterns in POEMS syndrome Blood, 2020, https://doi.org/10.1182/blood.2019004197

Roberta Cavagna et al., Capture-based Next-Generation Sequencing Improves the Identification of Immunoglobulin/T-Cell Receptor Clonal Markers and Gene Mutations in Adult Acute Lymphoblastic Leukemia Patients Lacking Molecular Probes Cancers, 2020, 12(6), 1505, https://doi.org/10.3390/cancers12061505

Frédéric Davi et al., on behalf of ERIC, the European Research Initiative on CLL, and the EuroClonality-NGS Working Group, Immunoglobulin gene analysis in chronic lymphocytic leukemia in the era of next generation sequencing 2020 Leukemia, 2020, https://doi.org/10.1038/s41375-020-0923-9

Yann Ferret et al., Multi-loci diagnosis of acute lymphoblastic leukaemia with high-throughput sequencing and bioinformatics analysis, British Journal of Haematology, 2016, 173, 413–420 https://hal.archives-ouvertes.fr/hal-01279160

Henrike J. Fischer et al., Modulation of CNS autoimmune responses by CD8+ T cells coincides with their oligoclonal expansion Journal of Neuroimmunology, 2015, S0165-5728(15)30065-5 http://dx.doi.org/10.1016/j.jneuroim.2015.10.020

Navarro Nilo Giusti et al., 2020 Test trial of spike-in immunoglobulin heavy-chain (IGH) controls for next generation sequencing quantification of minimal residual disease in acute lymphoblastic leukaemia British Journal of Haematology, 2020, 189: e150-e154 https://doi.org/10.1111/bjh.16571

Irene Jo et al., Considerations for monitoring minimal residual disease using immunoglobulin clonality in patients with precursor B-cell lymphoblastic leukemia, Clinica Chimica Acta, 2019, https://doi.org/10.1016/j.cca.2018.10.037

Takashi Kanamori et al., Genomic analysis of multiple myeloma using targeted capture sequencing in the Japanese cohort British Journal of Haematology, 2020, https://doi.org/10.1111/bjh.16720

Kenji Kimura et al., Identification of Clonal Immunoglobulin λ Light-Chain Gene Rearrangements in AL Amyloidosis Using Next Generation Sequencing, ASH 2019, https://doi.org/10.1182/blood-2019-125028

Michaela Kotrova et al., The predictive strength of next-generation sequencing MRD detection for relapse compared with current methods in childhood ALL, Blood, 2015, 126:1045-1047 http://dx.doi.org/10.1182/blood-2015-07-655159

Michaela Kotrova et al., Next‐generation amplicon TRB locus sequencing can overcome limitations of flow‐cytometric Vβ expression analysis and confirms clonality in all T‐cell prolymphocytic leukemia cases, Cytometry Part A, 93(11):1118-1124, 2018 http://dx.doi.org/10.1002/cyto.a.23604

Ralf A. Linker et al., Thymocyte-derived BDNF influences T-cell maturation at the DN3/DN4 transition stage European Journal of Immunology, 2015, 45, 1326-1338 http://dx.doi.org/10.1002/eji.201444985

Edit Porpaczy et al., Aggressive B-cell lymphomas in patients with myelofibrosis receiving JAK1/2 inhibitor therapy, Blood, 2018, https://dx.doi.org/10.1182/blood-2017-10-810739

Mikaël Salson et al., High-throughput sequencing in acute lymphoblastic leukemia: Follow-up of minimal residual disease and emergence of new clones, Leukemia Research, 2017, 53, 1–7 http://dx.doi.org/10.1016/j.leukres.2016.11.009

Masashi Sanada et al., Targeted-Capture Sequencing Is a Useful Method for MRD Markers Screening in KMT2A (MLL) Rearranged Leukemia Blood, 2019, 134(S1):2759 https://doi.org/10.1182/blood-2019-125421

Florian Scherer et al., Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA, Science Translational Medicine, 2016, 8, 364ra155 http://dx.doi.org/10.1126/scitranslmed.aai8545

V. Seitz et al., Evidence for a role of RUNX1 as recombinase cofactor for TCRβ rearrangements and pathological deletions in ETV6-RUNX1 ALL Scientific Reports, 2020, 10: 10024 https://doi.org/10.1038/s41598-020-65744-0

Udo zur Stadt et al., Characterization of novel, recurrent genomic rearrangements as sensitive MRD targets in childhood B-cell precursor ALL Blood Cancer Journal, 2019, https://doi.org/10.1038/s41408-019-0257-x

Lucia Stranavova et al., Heterologous Cytomegalovirus and Allo-Reactivity by Shared T Cell Receptor Repertoire in Kidney Transplantation Frontiers in Immunology, 2019, https://doi.org/10.3389/fimmu.2019.02549

Amelie Trinquand et al., Towards molecular stratification of pediatric T-cell lymphoblastic lymphomas based on Minimal Disseminated Disease and NOTCH1/FBXW7 mutational status: the French EURO-LB02 experience (preprint) medRxiv 2020.09.08.20189829, https://www.medrxiv.org/content/10.1101/2020.09.08.20189829v1

Gary Wright et al., Clinical benefit of a high‐throughput sequencing approach for minimal residual disease in acute lymphoblastic leukemia, Pediatric Blood & Cancer, 2019, https://doi.org/10.1002/pbc.27787

Wen‐Qing Yao et al., Angioimmunoblastic T‐cell lymphoma contains multiple clonal T‐cell populations derived from a common TET2 mutant progenitor cell The Journal of Pathology, 2019, https://doi.org/10.1002/path.5376

Yasuda et al., Clinical utility of target capture‐based panel sequencing in hematological malignancies: A multicenter feasibility study Cancer Science, 2020, 111(9):3367-3378, https://dx.doi.org/10.1111/cas.14552