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. 2021 Apr 6;12:2075. doi: 10.1038/s41467-021-22206-z

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Length distribution of all known VNTRs (red) and selected targeted VNTRs (blue) across the GRCh38 human genome in base pairs. b The genotyping pipeline. c Neural network architecture for each VNTR which uses a mapping of reads to a k-mer composition vector. d Improvement in running time after using neural network and k-mer matching. e Accuracy and efficiency of read recruitment in simulated data. The scatter plot shows 1-efficiency ((TP + FP)/R) and recall (TP/(TP + FN)) of classification with different methods. High efficiency is related directly with running time. Each of 10,264 points represents a VNTR locus (Method) and are shown once for each method. The side and top panels show cumulative distributions of recall and 1-efficiency. f Base pairs (log-scale) affected by VNTRs per individual in the GTEx cohort. Source data are provided as a Source Data file.