Cartwright Lab | Home
The Cartwright Lab
Computational Evolutionary Genetics Research
The Cartwright Lab is part of the Center for Evolutionary Medicine and Informatics in the Biodesign Institute at Arizona State University. We are located in Tempe, AZ USA in the Phoenix metro area. We focus on developing, implementing, and applying novel methodologies to study large, complex genomic datasets.
Our research tackles many different questions in population genetics and molecular evolution, at the interface of biology, statistics, and computer science.
- The study of mutation patterns between human families and across species, using data from next-generation sequencing. This includes both indel and point-mutation patterns.
- Models of frequency-dependent selection, with applications to genomic data.
- New methods for alignment and phylogeny reconstruction that take into account the uncertainty of genomic data.
- New methods for simulating homologous sequences that can be optimized to mimic natural datasets.
- Cartwright et al. (2012) A family-based probabilistic method for capturing de novo mutations from high-throughput short-read sequencing data. Statistical Applications in Genetics and Molecular Biology 11:6.
- Conrad et al. (2011) Variation in genome-wide mutation rates within and between human families. Nature Genetics 43:712–714.
- Lücking et al. (2011) PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination. BMC Bioinformatics 12:10.
- Cartwright et al. (2011) History Can Matter: Non-Markovian Behavior of Ancestral Lineages. Systematic Biology 60:276–290. [reprint]
- Price et al. (2011) Neutral evolution of robustness in Drosophila microRNA precursors. Molecular Biology and Evolution 28:2115–2123. [reprint]
- Cartwright (2011) Bards, poets, and cliques: Frequency-dependent selection and the evolution of language genes. Bulletin of Mathematical Biology 73:2201–2212. [reprint]
- The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature 467:1061–1073.
- Cartwright (2009) Problems and solutions for estimating indel rates and length distributions. Molecular Biology and Evolution 26:473–480. [reprint]
- Cartwright (2006) Logarithmic gap costs decrease alignment accuracy. BMC Bioinformatics 7:527. [reprint]
- Cartwright (2005) DNA assembly with gaps (Dawg): simulating sequence evolution. Bioinformatics 21(Suppl. 3):iii31–iii38. [reprint]