title: Learning HMMs for nucleotide sequences from amino acid alignments.
abstract: Profile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome.
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Carlos N
authors: CN
authors: Fischer
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Claudia M A
authors: CM
authors: Carareto
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Renato A C
authors: RA
authors: dos Santos
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Ricardo
authors: R
authors: Cerri
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Eduardo
authors: E
authors: Costa
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Leander
authors: L
authors: Schietgat
authors: Department of Statistics, Applied Maths, and Computer Science, UNESP - São Paulo State University, Rio Claro, SP, Brazil, Department of Biology, UNESP-São Paulo State University, São José do Rio Preto, SP, Brazil, Institute of Biosciences, UNESP-São Paulo State University, Rio Claro, SP, Brazil, Department of Computer Science, UFSCar-Federal University of São Carlos, São Carlos, SP, Brazil, Department of Computer Science, USP-University of São Paulo, São Carlos, SP, Brazil, Department of Computer Science, KU Leuven, Leuven, Belgium and Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium.
authors: Celine
authors: C
authors: Vens
mesh_terms: D000818:Animals; D001419:Bacteria / Q000201:enzymology / Q000235:genetics; D005656:Fungal Proteins / Q000737:chemistry / Q000235:genetics; D023281:Genomics / Q000379:methods*; D008390:Markov Chains; D010744:Phosphoric Monoester Hydrolases / Q000737:chemistry / Q000235:genetics; D017434:Protein Structure, Tertiary; D016914:Ribonuclease H / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D005656:Fungal Proteins; D016914:Ribonuclease H; D010744:Phosphoric Monoester Hydrolases