Creating a universal SNP and small indel variant caller with deep neural networks
By Ryan Poplin, Pi-Chuan Chang, David H. Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Daniel E. Newburger, Jojo Dijamco, Nam D. Nguyen, Pegah T. Afshar and 4 more. 2016
103 citations recorded by the source corpus.
Subfields: Genetics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Brett K. Beaulieu-Jones
- Casey S. Greene
- Michael C. Schatz
- Zhiyong Lu
- Eric J. Topol
- Jinbo Xu
- Daniel S. Himmelstein
- Hayan Lee
In Molecular Biology
- Casey S. Greene
- Yifan Peng
- Brett K. Beaulieu-Jones
- G. Way
- Amr M. Alexandari
- Fritz J. Sedlazeck
- Zhiyong Lu
- Avanti Shrikumar
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Taking this idea to the extreme, genomic data has been artificially encoded as images in order to benefit from pre-trained image classifiers [257].”
- High-performance medicine: the convergence of human and artificial intelligence (2019, background): “There has been much written about the black box of algorithms, and much controversy surrounding this topic 191–193 ; especially in the case of DNNs, it may not be possible to understand the determination of output.”
- Training Genotype Callers with Neural Networks (2016, methodology): “The computational procedure described in Poplin et al. [2016] has another important drawback: its computational efficiency.”
- Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “In 2016, Google and Verily Life Sciences published a pre-print describing DeepVariant – a deep learning-based universal SNP and small indel variant caller that won the "highest performance" award for the SNPs Administration-sponsored variant calling “Truth Challenge” in May 2016 [82].”
- Piercing the dark matter: bioinformatics of long-range sequencing and mapping (2018, methodology): “…made possible through advances in base calling and polishing, although new machine-learning techniques, such as advanced graphical models or deep-learning technologies, could be used to further improve sequence accuracy or improve the detection of genomic variants or epigenetic modifications 117 .”