A major focus at the NYGC is the development and application of computational methods capable of extracting biological understanding from genomics data. These tools are publicly available and many are widely used by the broader scientific community.
In particular, NYGC scientists have developed software for single-cell analysis (Satija Lab), structural variation calling (Imielinski Lab), RNA splicing quantification (Knowles Lab), allele-specific expression analysis (Lappalainen Lab), and somatic variant calling (Computational Biology / Bioinformatics group).
NYGC researchers are involved in using these, and other, computational and statistical approaches to further understand both healthy development and genetic disease.
Examples include whole genome sequencing analysis to probe the genetic basis of autism (Iossifov Lab and the Computational Biology / Bioinformatics group), integrating genetic and transcriptomic data to model gene regulation (Lappalainen and Knowles labs), and mapping epigenetic “mutations” in hematological cancers using single-cell methylation measurements (Landau Lab).
The Computational Biology / Bioinformatics group at the NYGC is a team of scientists, analysts and programmers who conduct research in cancer genomics, transcriptomics, genetics of Mendelian and complex diseases, statistical genetics, metagenomics, epigenomics, and genome and transcriptome assembly. This work includes the development of tools and analysis pipelines. The group works closely with the Software Engineering, Research Computing and Sequencing groups at NYGC to continuously improve and speed-up the analysis of genomic data. This group also collaborates with the NYGC faculty labs, the NYGC Technology Innovation Lab, and external researchers, with the goal of translating improvements in genomic analysis methods into better data to guide more informed healthcare.