Jie Zhang
Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Publications
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Research Article
Findel: A Deep Learning Approach To Efficient Artifact Removal from Cancer Genomes
Author(s): Denis Tan, Pengfei Zhou, Shaoting Zhang, VicPearly Wong, Jie Zhang* and Edwin Long*
Next-generation sequencing technologies have increased sequencing throughput by 100-1000 folds and subsequently reduced the cost of sequencing a human genome to approximately US$1,000. However, the existence of sequencing artifacts can cause erroneous identification of variants and adversely impact the downstream analyses. Currently, the manual inspection of vari- ants for additional refinement is still necessary for high-quality variant calls. The inspection is usually done on large binary alignment map (BAM) files which consume a huge amount of labor and time. It also suffers from a lack of standardization and reproducibility. Here we show that the use of mutational signatures coupled with deep learning can replace the current stan- dards in the bioinformatics workflow. This software, called FINDEL, can efficiently remove sequencing artifacts from cancer samples. It queries the vari.. Read More»