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. 2021 Feb 24;22:133. doi: 10.1186/s12864-021-07395-7

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — MONTAGE Algorithm Conceptual Flow Diagram. Code overview with key inputs and variables. First data is normalized to BAF and LRR values genome-wide for each sample. Then the number of deviating BAF values in a first pass sliding window are accounted to screen for potential mosaic events as an initial algorithmic step. The first quartile minus 1.5 interquartile range of the LRR paired with strong BAF deviation defines the calling threshold for mosaic deletion events. Fragmented windows meeting threshold are then merged together to form larger calls. The breakpoints are then refined based on the first and last specific base pair evidence of strongly deviating BAF within the merged window