RT Journal Article SR Electronic T1 Targeted variant detection using unaligned RNA-Seq reads JF Life Science Alliance JO Life Sci. Alliance FD Life Science Alliance LLC SP e201900336 DO 10.26508/lsa.201900336 VO 2 IS 4 A1 Eric Olivier Audemard A1 Patrick Gendron A1 Albert Feghaly A1 Vincent-Philippe Lavallée A1 Josée Hébert A1 Guy Sauvageau A1 Sébastien Lemieux YR 2019 UL https://www.life-science-alliance.org/content/2/4/e201900336.abstract AB Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by 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 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 the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.