DePristo, Mark A.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research

Genomics and Phylogenetic StudiesScientific Computing and Data Management

Cited by 27,895

From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline

Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 6,687

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 5,312

Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome

Wenger, A. M., Peluso, P., Rowell, W. J., Chang, P.-C., Hall, R. J., Concepcion, G. T., Ebler, J., Fungtammasan, A., Kolesnikov, A., Olson, N. D., et al. (2019) · Nature Biotechnology

Genomics and Phylogenetic StudiesGenomic variations and chromosomal abnormalities

Cited by 1,841

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 1,840

A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes

MacArthur, D. G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J. K., Montgomery, S. B., et al. (2012) · Science

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 1,272

Using deep learning to annotate the protein universe

Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology

Machine Learning in BioinformaticsGenomics and Phylogenetic Studies

Cited by 332

Relation between native ensembles and experimental structures of proteins

Best, R. B., Lindorff-Larsen, K., DePristo, M. A., & Vendruscolo, M. (2006) · Proceedings of the National Academy of Sciences

Protein Structure and DynamicsAdvanced NMR Techniques and Applications

Cited by 152