Profile HMM based Multiple Sequence Alignment for DNA Sequences

Abstract

Sequence alignment is a central tool in molecular biology. A Multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA or RNA to identify regions of similarity that may be a consequence of functional, structural or evolutionary relationships between the sequences. High sequence similarity between molecules usually implies significant structural and functional similarities in an alignment. Three or more sequences of biologically relevant length can be difficult and are almost always time-consuming to align by hand, computational algorithms are used to produce and analyze the alignments. MSAs require more sophisticated methodologies because they are more computationally complex. A hidden markov model (HMM) is a probabilistic finite state machine which is widely used in biological sequence analysis. Profile HMMs are specific types of HMM used in biological sequence analysis. In this paper, we show how Profile HMMs can be useful for multiple sequence alignment. We test their applicability to the tasks of multiple alignments and find that they work well.