Abstract:
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.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Giraud M,Salson M,Duez M,Villenet C,Quief S,Caillault A,Grardel N,Roumier C,Preudhomme C,Figeac Mdoi
10.1186/1471-2164-15-409subject
Has Abstractpub_date
2014-05-28 00:00:00pages
409issn
1471-2164pii
1471-2164-15-409journal_volume
15pub_type
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