Exploring the limit of using a deep neural network on pileup data for germline variant calling
| Autor: | Chak-Lim Wong, Yat-Sing Wong, Ruibang Luo, Chi-Ming Leung, Tak-Wah Lam, Chi-Ian Tang, Chi-Man Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Artificial neural network Computer Networks and Communications business.industry Computer science Sequencing data Sequence assembly Machine learning computer.software_genre Germline Human-Computer Interaction 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Artificial Intelligence Deep neural networks Computer Vision and Pattern Recognition Nanopore sequencing Artificial intelligence Limit (mathematics) Central processing unit business computer 030217 neurology & neurosurgery Software |
| Zdroj: | Nature Machine Intelligence. 2:220-227 |
| ISSN: | 2522-5839 |
| DOI: | 10.1038/s42256-020-0167-4 |
| Popis: | Single-molecule sequencing technologies have emerged in recent years and revolutionized structural variant calling, complex genome assembly and epigenetic mark detection. However, the lack of a highly accurate small variant caller has limited these technologies from being more widely used. Here, we present Clair, the successor to Clairvoyante, a program for fast and accurate germline small variant calling, using single-molecule sequencing data. For Oxford Nanopore Technology data, Clair achieves better precision, recall and speed than several competing programs, including Clairvoyante, Longshot and Medaka. Through studying the missed variants and benchmarking intentionally overfitted models, we found that Clair may be approaching the limit of possible accuracy for germline small variant calling using pileup data and deep neural networks. Clair requires only a conventional central processing unit (CPU) for variant calling and is an open-source project available at https://github.com/HKU-BAL/Clair. A lack of accurate and efficient variant calling methods has held back single-molecule sequencing technologies from clinical applications. The authors present a deep-learning method for fast and accurate germline small variant calling, using single-molecule sequencing data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink