A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

• Published in: Nature genetics Vol. 50; no. 12; pp. 1735 - 1743
• Main Authors: Ainscough, Benjamin J., Barnell, Erica K., Ronning, Peter, Campbell, Katie M., Wagner, Alex H., Fehniger, Todd A., Dunn, Gavin P., Uppaluri, Ravindra, Govindan, Ramaswamy, Rohan, Thomas E., Griffith, Malachi, Mardis, Elaine R., Swamidass, S. Joshua, Griffith, Obi L.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2018; Nature Publishing Group
• Subjects: 45; 631/114/1314; 631/114/2785; 631/114/794; 631/208/514/2184; 692/699/67; Agriculture; Algorithms; Animal Genetics and Genomics; Artificial intelligence; Automation; Biomedical and Life Sciences; Biomedicine; Breast cancer; Cancer; Cancer genetics; Cancer Research; Computer Simulation; Data processing; Datasets; Deep Learning; DNA Mutational Analysis - instrumentation; DNA Mutational Analysis - methods; Electronic Data Processing - methods; Gene Function; Genetic variation; Genomes; Genomic analysis; Genomics; Handbooks; High-Throughput Nucleotide Sequencing - instrumentation; Human Genetics; Humans; Learning algorithms; Leukemia; Machine learning; Mutation; Neoplasms - genetics; Polymorphism, Single Nucleotide; Quality standards; Reproducibility of Results; Sequence Analysis, DNA - instrumentation; Sequence Analysis, DNA - methods; Software; technical-report; Technology application; Tumors

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducib...