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Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/luojunwei\/BreakNet\">https:\/\/github.com\/luojunwei\/BreakNet<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04499-5","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:04:36Z","timestamp":1638453876000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["BreakNet: detecting deletions using long reads and a deep learning approach"],"prefix":"10.1186","volume":"22","author":[{"given":"Junwei","family":"Luo","sequence":"first","affiliation":[]},{"given":"Hongyu","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jiquan","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Haixia","family":"Zhai","sequence":"additional","affiliation":[]},{"given":"Zhengjiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chaokun","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Huimin","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"4499_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/nature15394","volume":"526","author":"P Sudmant","year":"2015","unstructured":"Sudmant P, Rausch T, Gardner E, et al. 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