Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Combining phylogenetic and hidden Markov models in biosequence analysis
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
The Number of Recombination Events in a Sample History: Conflict Graph and Lower Bounds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Journal of Computer and System Sciences - Special issue on bioinformatics II
The Fine Structure of Galls in Phylogenetic Networks
INFORMS Journal on Computing
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Minimum recombination histories by branch and bound
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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Large amount of population-scale genetic variation data are being collected in populations. One potentially important biological problem is to infer the population genealogical history from these genetic variation data. Partly due to recombination, genealogical history of a set of DNA sequences in a population usually cannot be represented by a single tree. Instead, genealogy is better represented by a genealogical network, which is a compact representation of a set of correlated local genealogical trees, each for a short region of genome and possibly with different topology. Inference of genealogical history for a set of DNA sequences under recombination has many potential applications, including association mapping of complex diseases.In this paper, we present two new methods for reconstructing local tree topologies with the presence of recombination, which extend and improve the previous work in. We first show that the "tree scan” method can be converted to a probabilistic inference method based on a hidden Markov model. We then focus on developing a novel local tree inference method called RENT that is both accurate and scalable to larger data. Through simulation, we demonstrate the usefulness of our methods by showing that the hidden-Markov-model-based method is comparable with the original method interms of accuracy. We also show that RENT is competitive with other methods in terms of inference accuracy, and its inference error rate is often lower and can handle large data.