Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing

BMC Genomics

Martin Figeac¹  Claude Preudhomme⁴  Christophe Roumier⁴  Nathalie Grardel⁵  Aurélie Caillault⁵  Sabine Quief²  Céline Villenet¹  Marc Duez⁶  Mikaël Salson³  Mathieu Giraud³ 

[1] Functional and Structural Genomic Platform, Université Lille 2, IFR 114, Lille, France;Lille Institute for Cancer Research (IRCL), Lille, France;Laboratoire d’Informatique Fondamentale de Lille (LIFL, UMR CNRS 8022, Université Lille 1) and Inria Lille – Cité scientifique – Bâtiment M3, 59655 Villeneuve d’Ascq, France;Inserm U-837, Cancer Research Institute, Lille, France;Department of Hematology, Biology and Pathology Center, Lille, France;SIRIC OncoLille, Lille, France

关键词: Minimal residual disease follow-up;    Leukemia;    Immunology;    Repertoire sequencing;    V(D)J recombinations;    High-throughput sequencing;    Sequence analysis;   

Others  :  1217188
DOI  :  10.1186/1471-2164-15-409

 received in 2013-10-23, accepted in 2014-05-08,  发布年份 2014

【 摘 要 】

Background

V(D)J recombinations in lymphocytes are essential for immunological diversity. They are also useful markers of pathologies. In leukemia, they are used to quantify the minimal residual disease during patient follow-up. However, the full breadth of lymphocyte diversity is not fully understood.

Results

We propose new algorithms that process high-throughput sequencing (HTS) data to extract unnamed V(D)J junctions and gather them into clones for quantification. This analysis is based on a seed heuristic and is fast and scalable because in the first phase, no alignment is performed with germline database sequences. The algorithms were applied to TR γ HTS data from a patient with acute lymphoblastic leukemia, and also on data simulating hypermutations. Our methods identified the main clone, as well as additional clones that were not identified with standard protocols.

Conclusions

The proposed algorithms provide new insight into the analysis of high-throughput sequencing data for leukemia, and also to the quantitative assessment of any immunological profile. The methods described here are implemented in a C++ open-source program called Vidjil.

【 授权许可】

   
2014 Giraud et al.; licensee BioMed Central Ltd.

【 图 表 】

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