Bio++: Efficient Extensible Libraries and Tools for Computational Molecular Evolution

Efficient algorithms and programs for the analysis of the ever-growing amount of biological sequence data are strongly needed in the genomics era. The pace at which new data and methodologies are generated calls for the use of pre-existing, optimized—yet extensible—code, typically distributed as libraries or packages. This motivated the Bio++ project, aiming at developing a set of C++ libraries for sequence analysis, phylogenetics, population genetics, and molecular evolution. The main attractiveness of Bio++ is the extensibility and reusability of its components through its object-oriented design, without compromising the computer-efficiency of the underlying methods. We present here the second major release of the libraries, which provides an extended set of classes and methods. These extensions notably provide built-in access to sequence databases and new data structures for handling and manipulating sequences from the omics era, such as multiple genome alignments and sequencing reads libraries. More complex models of sequence evolution, such as mixture models and generic n-tuples alphabets, are also included

Production de jeunes bovins maigres ou engraissés en Limousin : campagne 1987-88

[Coll_IRSTEA]Coll. Etudes du Cemagref, Groupement de Clermont-Ferrand, n° 12

Using Colored Petri Nets to Construct Coalescent Hidden Markov Models: Automatic Translation from Demographic Specifications to Efficient Inference Methods

Abstract. Biotechnological improvements over the last decade has made it economically and technologically feasible to collect large DNA sequence data from many closely related species. This enables us to study the detailed evolutionary history of recent speciation and demographics. Sophisticated statistical methods are needed, however, to extract the information that DNA sequences hold, and a limiting factor in this is dealing with the large state space that the ancestry of large DNA sequences spans. Recently a new analysis method, CoalHMMs, has been developed, that makes it computationally feasible to scan full genome sequences – the complete genetic information of a species – and extract genetic histories from this. Applying this methodology, however, requires that the full state space of ancestral histories can be constructed. This is not feasible to do manually, but by applying formal methods such as Petri nets it is possible to build sophisticated evolutionary histories and automatically derive the analysis models needed. In this paper we describe how to use colored stochastic Petri nets to build CoalHMMs for complex demographic scenarios.