Cancer Genomics: Computational & Experimental Tools and Methods Developed by the Meyerson Lab
The Meyerson lab has developed many algorithms and experimental approaches in cancer genomics that are widely used by our laboratory and by the broader scientific community.
The Meyerson lab has developed many algorithms and experimental approaches in cancer genomics that are widely used by our laboratory and by the broader scientific community.
Innovations in experimental cancer genomics:
Novel analytical methods for cancer genome analysis:
- First use of SNP arrays in cancer genome analysis, both for loss of heterozygosity (LIndblad-Toh et al., Nat. Biotechnol., 2000) and for copy number alterations (Zhao et al., Cancer Res, 2004)
- First application of next-generation sequencing to cancer (Thomas et al., Nat Med., 2006)
- CRISPR-driven genome editing approaches to model long-range chromosomal rearrangements in cancer (Choi and Meyerson, Nat Commun., 2014)
- Novel experimental methods for single cell sequencing of tumors (Francis et al., Cancer Discov, 2014)
Novel analytical methods for cancer genome analysis:
- Computational subtraction method for pathogen discovery in cancer (Weber et al., Nat Genetics, 2002)
- GISTIC, for analysis of copy number alterations in cancer (Beroukhim et al., PNAS, 2007)
- PathSeq, applying the computational subtraction method to next-generation sequencing data for pathogen detection in human tissue specimens (Kostic et al., Nat Biotechnol., 2011)
- ABSOLUTE, to assess tumor purity and ploidy (Carter et al., Nat Biotechnol., 2012)
- TranspoSeq, an algorithm to detect retrotransposon insertions in cancer genomes (Helman et al., Genome Res., 2014)
- Analytical tools to correct for genome amplification bias in single cell sequencing (Zhang et al., Nat Commun., 2015)
- Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors (Adalsteinsson et al., Nat Commun., 2017)