A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes

Background: Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events. Results: We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied ou...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Anne-Kathrin Schultz, Ming Zhang, Thomas Leitner, Carla Kuiken, Bette T. Korber, Burkhard Morgenstern, Mario Stanke
• Keywords: BMCBI 2006, Markov Models, Multiple Alignment, Sequences