https://scholars.lib.ntu.edu.tw/handle/123456789/594622
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Phan, Nam Nhut | en_US |
dc.contributor.author | Chattopadhyay, Amrita | en_US |
dc.contributor.author | Lee, Tsui-Ting | en_US |
dc.contributor.author | Yin, Hsiang-I | en_US |
dc.contributor.author | TZU-PIN LU | en_US |
dc.contributor.author | Liang-Chuan Lai | en_US |
dc.contributor.author | HSIAO-LIN HWA | en_US |
dc.contributor.author | MONG-HSUN TSAI | en_US |
dc.contributor.author | ERIC YAO-YU CHUANG | en_US |
dc.date.accessioned | 2022-02-15T06:22:53Z | - |
dc.date.available | 2022-02-15T06:22:53Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 14774054 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121953243&doi=10.1093%2fbib%2fbbab283&partnerID=40&md5=dbf439400563e85ce3236af7c7690e68 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/594622 | - |
dc.description.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. © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. | - |
dc.publisher | NLM (Medline) | - |
dc.relation.ispartof | Briefings in bioinformatics | - |
dc.subject | breast cancer; deep learning; DNA mixture; forensic; next-generation sequencing | - |
dc.subject.other | 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 | - |
dc.title | High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1093/bib/bbab283 | - |
dc.identifier.pmid | 34368845 | - |
dc.identifier.scopus | 2-s2.0-85121953243 | - |
dc.relation.journalvolume | 22 | - |
dc.relation.journalissue | 6 | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
crisitem.author.dept | Institute of Health Data Analytics and Statistics | - |
crisitem.author.dept | Public Health | - |
crisitem.author.dept | Physiology | - |
crisitem.author.dept | Forensic Medicine | - |
crisitem.author.dept | Medical Genetics-NTUH | - |
crisitem.author.dept | Obstetrics & Gynecology-NTUH | - |
crisitem.author.dept | Biotechnology | - |
crisitem.author.dept | Center for Biotechnology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Biomedical Electronics and Bioinformatics | - |
crisitem.author.dept | Center for Biotechnology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.orcid | 0000-0003-3697-0386 | - |
crisitem.author.orcid | 0000-0002-3913-5338 | - |
crisitem.author.orcid | 0000-0002-8094-1163 | - |
crisitem.author.orcid | 0000-0001-8777-5818 | - |
crisitem.author.orcid | 0000-0003-2530-0096 | - |
crisitem.author.parentorg | College of Public Health | - |
crisitem.author.parentorg | College of Public Health | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Bioresources and Agriculture | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
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