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Open Access Software

PyEvolve: a toolkit for statistical modelling of molecular evolution

Andrew Butterfield1, Vivek Vedagiri2, Edward Lang1, Cath Lawrence1, Matthew J Wakefield13, Alexander Isaev1 and Gavin A Huttley1*

Author Affiliations

1 Centre for Bioinformation Science, John Curtin School of Medical Research and Mathematical Sciences Institute, Australian National University, Canberra, ACT 0200, Australia

2 HeliXense Pte. Ltd., 73, Science Park Drive, #02-05, CINTECH 1, Science Park 1, Singapore 118254

3 ARC Centre for Kangaroo Genomics, Research School of Biological Sciences, The Australian National University, Canberra, ACT 0200, Australia

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BMC Bioinformatics 2004, 5:1  doi:10.1186/1471-2105-5-1

Published: 5 January 2004

Abstract

Background

Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences – ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation.

Results

Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from ~10 days to ~6 hours.

Conclusion

PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software webcite.