Abstract

An explosive growth is occurring in the quantity, quality and complexity of molecular variation data that are being collected. Historically, such data have been analysed by using model-based methods. Models are useful for sharpening intuition, for explanation and for prediction: they add to our understanding of how the data were formed, and they can provide quantitative answers to questions of interest. We outline some of these model-based approaches, including the coalescent, and discuss the applicability of the computational methods that are necessary given the highly complex nature of current and future data sets.

Author affiliations

1. University of Southern California, Keck School of Medicine, Preventive Medicine, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-99011, USA.
2. Program in Molecular and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, California 90089-2910, USA, and Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK.

Correspondence to: Simon Tavaré^1,2 Email: stavare@usc.edu

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