Efficient algorithms for lateral gene transfer problems
RECOMB '01 Proceedings of the fifth annual international conference on 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
Simultaneous identification of duplications and lateral transfers
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Maximum likelihood of phylogenetic networks
Bioinformatics
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
RIATA-HGT: a fast and accurate heuristic for reconstructing horizontal gene transfer
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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Horizontal Gene Transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimericHGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently.In this work we suggest a new probabilistic model for analyzing and modeling phylogenetic networks, the NET-HMM. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are notindependent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given data set.Using NET-HMM we are able to answer interesting biological questions, such as inferring the length of partial HGT's and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and two different biological inputs.