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bioRxiv · 10.1101/2025.01.17.631891

KILDA: identifying KIV-2 repeats from kmers

Molitor, C.·Labidi, T.·Rimbert, A.·Cariou, B.·Di Filippo, M.·Bardel, C.
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Abstract

MotivationHigh concentration of lipoprotein(a), a lipoprotein with proatherogenic properties, is an important risk factor for cardiovascular disease. This concentration is mostly genetically determined by a complex interplay between the number of Kringle-IV type 2 repeats and Lipoprotein(a)-affecting variants. Besides lipoprotein(a) plasma concentration, there is an unmet need to identify individuals most at risk based on their LPA genotype. ResultsWe developed KILDA, a Nextflow pipeline, to identify the number of Kringle-IV type 2 repeats and Lp(a)-affecting variants directly from kmers generated from FASTQ files. The pipeline was tested on the 1000 Genomes Project (n=2459) and results were equivalent to DRAGEN-LPA (R2=0.93). In-silico datasets proved the robustness of KILDAs predictions under different scenarios of sequencing coverage and quality. ConclusionKILDA is an open-source and free-to-use pipeline to identify the number of Kringle-IV type 2 repeats and lipoprotein(a)-associated variants. Its results are equivalent to DRAGEN-LPA, offering a free and robust tool for determining the LPA kringle number even when inputting low coverage libraries. AvailabilityKILDA is publicly available at https://github.com/HCL-HUBL/KILDA along with a recipe to build an Apptainer image containing all the required dependencies. Contact: corentin.molitor@chu-lyon.fr Supplementary informationSupplementary data are available at Bioinformatics online.

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Molitor, C., Labidi, T., Rimbert, A., Cariou, B., Di Filippo, M., Bardel, C.. 2025-01-18. KILDA: identifying KIV-2 repeats from kmers. https://doi.org/10.1101/2025.01.17.631891

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