The large array of ‘omics measurement methods holds great promise for making medicine increasingly precise and personalized — by using genome-scale molecular diagnostics to ensure that patients get the right treatment at the right time, for their condition. To help enable clinical translation of ‘omics measurements (including genomics, transcriptomics, epigenomics, and proteomics), our group is developing well-characterized samples and measurement methods to understand biases and errors of measurement technologies and bioinformatics methods. Current interests include integrating data from multiple DNA sequencing datasets for variant cells (SNPs, indels, structural variants, haplotype phasing, HLA typing), cancer sequencing, cell-free DNA sequencing, RNA sequencing (mRNA and miRNA), proteomics, and exploring new measurement technologies. We are also interested in improving methods to integrate these and other ‘omics measurements using machine learning and biostatistics to present the large quantities of data to physicians.
This research will be conducted at NIST in Gaithersburg, MD and/or Stanford University in Palo Alto, CA.
Genomics; Bioinformatics; Machine learning; Statistics; Standards; Biology; Transcriptomics; Data science; Proteomics;