Alexander Isaev

PyEvolve: a toolkit for statistical modelling of molecular evolution.

BackgroundExamining 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.ResultsHere 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 CpGs, 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.ConclusionPyEvolve 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.

Effective actions of the unitary group on complex manifolds

We classify all connected n-dimensional complex manifolds admitting effective actions of the unitary group Un by biholomorphic transformations. One consequence of this classification is a characterization of C n by its automorphism group.

Hyperbolic 2-dimensional manifolds with 3-dimensional automorphism group

In this paper we determine all Kobayashi-hyperbolic 2-dimensional complex manifolds for which the group of holomorphic automorphisms has dimension 3. This work concludes a recent series of papers by the author on the classification of hyperbolic

Hyperbolic manifolds of dimensionn with automorphism group of dimensionn 2 − 1

n

Finite type conditions on reinhardt domains

-dimensional manifolds, with automorphism group of dimension at least

Characterization of the unit ball in Cn among complex manifolds of dimensionn

n^2-1

Associated forms and hypersurface singularities: The binary case

, where

Examples of unbounded homogeneous domains in complex space

n\ge 2

On hyperbolic n-dimensional manifolds with automorphism group of dimension n2

.

Rigid spherical hypersurfaces

We consider complex Kobayashi-hyperbolic manifolds of dimension n ≥ 2 for which the dimension of the group ofholomorphic automorphisms is equal to n2 − 1. We give a complete classification of such manifolds for n ≥ 3 and discuss several examples for n = 2.