Author:
Boudry Augustin,Darmon Sasha,Duployez Nicolas,Figeac Martin,Geffroy Sandrine,Bucci Maxime,Celli-Lebras Karine,Duchmann Matthieu,Joudinaud Romane,Fenwarth Laurène,Nibourel Olivier,Goursaud Laure,Itzykson Raphael,Dombret Hervé,Hunault Mathilde,Preudhomme Claude,Salson Mikaël
Abstract
AbstractBackgroundInternal tandem duplications in theFLT3gene, termedFLT3-ITDs, are useful molecular markers in acute myeloid leukemia (AML) for patient risk stratification and follow-up.FLT3-ITDs are increasingly screened through high-throughput sequencing (HTS) raising the need for robust and efficient algorithms. We developed a new algorithm, which performs no alignment and uses little resources, to identify and quantifyFLT3-ITDs in HTS data.ResultsOur algorithm (FiLT3r) focuses on thek-mers from reads coveringFLT3exons 14 and 15. We show that thosek-mers bring enough information to accurately detect, determine the length and quantifyFLT3-ITD duplications. We compare the performances of FiLT3r to state-of-the-art alternatives and to fragment analysis, the gold standard method, on a cohort of 185 AML patients sequenced with capture-based HTS. On this dataset FiLT3r is more precise (no false positive nor false negative) than the other software evaluated. We also assess the software on public RNA-Seq data, which confirms the previous results and shows that FiLT3r requires little resources compared to other software.ConclusionFiLT3r is a free software available athttps://gitlab.univ-lille.fr/filt3r/filt3r. The repository also contains a Snakefile to reproduce our experiments. We show that FiLT3r detects FLT3-ITDs better than other software while using less memory and time.
Publisher
Cold Spring Harbor Laboratory
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