High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data. Issue 6 (7th August 2021)

• Record Type: Journal Article
• Authors: Phan, Nam Nhut; Chattopadhyay, Amrita; Lee, Tsui-Ting; Yin, Hsiang-I; Lu, Tzu-Pin; Lai, Liang-Chuan; Hwa, Hsiao-Lin; Tsai, Mong-Hsun; Chuang, Eric Y
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-08-07
• Subjects: deep learning -- next-generation sequencing -- forensic -- breast cancer -- DNA mixture; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab283
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 single nucleotide polymorphisms obtained through massively parallel sequencing protocol. The model was trained/tested/validated with sequenced data from 6 individuals and then evaluated using mixtures from forensic DNA samples. The model successfully identified both the major and the minor contributors with 100% accuracy for 90 DNA mixtures, that were manually prepared by mixing sequence reads of 3 individuals at different ratios. Furthermore, the model identified 100% of the major contributors and 50–80% of the minor contributors in 20 two-sample external-mixed-samples at ratios of 1:39 and 1:9, respectively. To further demonstrate the versatility and applicability of the pipeline, we tested it on whole exome sequence data to classify subtypes of 20 breast cancer patients and achieved an area under curve of 0.85. Overall, we present, for the first time, a complete pipeline, including sequencing data processing steps and DL steps, that is applicable across different NGS platforms. We also introduced a sliding window approach, to overcome the sequence length variation problem of sequencing data, and demonstrate that it improves the model performance dramatically.