Target variant detection in leukemia using unaligned RNA-Seq reads


Mutations identified in each Acute Myeloid Leukemia (AML) patients are useful for prognosis and to select targeted therapies. Detection of such mutations by the analysis of Next-Generation Sequencing (NGS) data requires a computationally intensive read mapping step and application of several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent AML cohorts (The Cancer Genome Atlas and Leucegene), to show that mutation detection by km is fast, accurate and mainly limited by sequencing depth. Therefore, km allows to establish fast diagnostics from NGS data, and could be suitable for clinical applications.,

Sébastien Lemieux
Sébastien Lemieux
Chercheur principal

Chercheur principal, Unité de recherche en bio-informatique fonctionnelle et structurale, IRIC | Direction scientifique de la plateforme de Bio-informatique | Professeur agrégé, Département de biochimie et médecine moléculaire, Université de Montréal