Abstract
BackgroundNon-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants. Unraveling the functional significance of non-coding DNA depends on how well we are able to align non-coding DNA sequences. However, the alignment of non-coding DNA sequences is more difficult than aligning protein-coding sequences.ResultsHere we present an improved pair-hidden-Markov-Model (pair HMM) based method for performing global pairwise alignment of non-coding DNA sequences. The method uses an explicit model of indel length frequency distribution which can be specified, and allows any time reversible model of nucleotide substitution. The method uses a deterministic global optimiser to find the alignment with the highest posterior probability. We test MCALIGN2 in simulations, and compare it to a previous Monte Carlo based method (MCALIGN), to the pair HMM method of Knudsen and Miyamoto, and to a heuristic method (AVID) that performed very well in a previous simulation study. We show that the pair HMM methods have excellent performance for all combinations of parameter values we have considered. MCALIGN2 is up to ten times faster than MCALIGN. MCALIGN2 is more accurate in resolving indels given an accurate explicit model than heuristic methods, but is computationally slower.ConclusionMCALIGN2 produces better quality alignments by explicitly using biological knowledge about the indel length distribution and time reversible models of nucleotide substitution. As a result, it can outperform other available sequence alignment methods for the cases we have considered to align non-coding DNA sequences.
Highlights
Non-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants
We compared MCALIGN2 against AVID of Bray et al [17] in simulations assuming a GTR model, parameterised using the Drosophila intronic data as described above. In these comparisons we investigated cases in which the model assumed by MCALIGN2 differed from the simulation model, by using the simpler JC and Kimura's 2 parameter (K2P) models to analyse data simulated under a GTR model
Comparison amongst PairHMM_KM, MCALIGN2(DP) and MCALIGN(MC) We generated non-coding sequence data using a model of non-coding DNA evolution in which gap lengths are parametrized by intronic data of D. simulans and D. sechellia, and point substitutions occur according to the Jukes-Cantor model to compare the performances of PairHMM_KM, MCALIGN2 (DP hereafter) and MCALIGN (MC hereafter)
Summary
Non-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants. Protein-coding gene sequences comprise as little as 1–2% of the human and mouse genomes [2,3]. There is an increasing body of evidence showing that non-coding DNA sequences contain many functional sequences involved in gene regulation and potentially other unknown functions. It has been inferred that the total number of selectively constrained nucleotides in non-coding DNA adjacent to gene sequences is similar to that in coding DNA [5], Evidence for the presence of a large number of potentially functional non-coding sequences on human chromosome 21 has recently been obtained from a comparative genomics analysis [6]. Determining the fraction of non-coding DNA that is functional and establishing what that function is, is a central problem in genome research
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