https://scholars.lib.ntu.edu.tw/handle/123456789/594622
標題: | High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data | 作者: | Phan, Nam Nhut Chattopadhyay, Amrita Lee, Tsui-Ting Yin, Hsiang-I TZU-PIN LU Liang-Chuan Lai HSIAO-LIN HWA MONG-HSUN TSAI ERIC YAO-YU CHUANG |
關鍵字: | breast cancer; deep learning; DNA mixture; forensic; next-generation sequencing | 公開日期: | 2021 | 出版社: | NLM (Medline) | 卷: | 22 | 期: | 6 | 來源出版物: | Briefings in bioinformatics | 摘要: | 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. © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121953243&doi=10.1093%2fbib%2fbbab283&partnerID=40&md5=dbf439400563e85ce3236af7c7690e68 https://scholars.lib.ntu.edu.tw/handle/123456789/594622 |
ISSN: | 14774054 | DOI: | 10.1093/bib/bbab283 | SDG/關鍵字: | DNA; DNA sequence; genetics; high throughput sequencing; human; procedures; single nucleotide polymorphism; Deep Learning; DNA; High-Throughput Nucleotide Sequencing; Humans; Polymorphism, Single Nucleotide; Sequence Analysis, DNA |
顯示於: | 生理學科所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。