A method for detecting distant evolutionary relationships between protein or nucleic acid sequences in the presence of deletions or insertions

Statistical evaluation and comparison of a pairwise alignment algorithm that a priori assigns the number of gaps rather than employing gap penalties

MOTIVATION

Although pairwise sequence alignment is essential in comparative genomic sequence analysis, it has proven difficult to precisely determine the gap penalties for a given pair of sequences. A common practice is to employ default penalty values. However, there are a number of problems associated with using gap penalties. First, alignment results can vary depending on the gap penalties, making it difficult to explore appropriate parameters. Second, the statistical significance of an alignment score is typically based on a theoretical model of non-gapped alignments, which may be misleading. Finally, there is no way to control the number of gaps for a given pair of sequences, even if the number of gaps is known in advance.

RESULTS

In this paper, we develop and evaluate the performance of an alignment technique that allows the researcher to assign a priori set of the number of allowable gaps, rather than using gap penalties. We compare this approach with the Smith-Waterman and Needleman-Wunsch techniques on a set of structurally aligned protein sequences. We demonstrate that this approach outperforms the other techniques, especially for short sequences (56-133 residues) with low similarity (<25%). Further, by employing a statistical measure, we show that it can be used to assess the quality of the alignment in relation to the true alignment with the associated optimal number of gaps.

AVAILABILITY

The implementation of the described methods SANK_AL is available at http://cbbc.murdoch.edu.au/

CONTACT

matthew@cbbc.murdoch.edu.au.

Prediction of oligonucleotide frequencies based upon dinucleotide frequencies obtained from the nearest neighbor analysis

A statistical method of predicting hexanucleotide frequencies is presented. The method requires dinucleotide frequencies which can be readily obtained by nearest neighbor analysis. The frequencies of 64 hexanucleotides of E. coli were estimated and compared well with those predicted by a third order Markov chain.

Mono- through hexanucleotide composition of the Escherichia coli genome: a Markov chain analysis

Several statistical methods were tested for accuracy in predicting observed frequencies of di- through hexanucleotides in 74,444 bp of E. coli DNA. A Markov chain was most accurate overall, whereas other methods, including a random model based on mononucleotide frequencies, were very inaccurate. When ranked highest to lowest abundance, the observed frequencies of oligonucleotides up to six bases in length in E. coli DNA were highly asymmetric. All ordered abundance plots had a wide linear range containing the majority of the oligomers which deviated sharply at the high and low ends of the curves. In general, values predicted by a Markov chain closely followed the overall shape of the ordered abundance curves. A simple equation was derived by which the frequency of any nucleotide longer than four bases in the E. coli genome (or any genome) can be relatively accurately estimated from the nested set of component tri- and tetranucleotides by serial application of a 3rd order Markov chain. The equation yielded a mean ratio of 1.03 +/- 0.94 for the observed-to-expected frequencies of the 4,096 hexanucleotides. Hence, the method is a relatively accurate but not perfect predictor of the length in nucleotides between hexanucleotide sites. Higher accuracy can be achieved using a 4th order Markov chain and larger data sets. The high asymmetry in oligonucleotide abundance means that in the E. coli genome of 4.2 X 10(6) bp many relatively short sequences of 7-9 bp are very rare or absent.

A comprehensive package for DNA sequence analysis in FORTRAN IV for the PDP-11

A computer package written in Fortran-IV for the PDP-11 minicomputer is described. The package's novel features are: software for voice-entry of sequence data; a less memory intensive algorithm for optimal sequence alignment; and programs that fit statistical models to nucleic acid and protein sequences.

Comparison of the nucleotide sequence of soybean 18S rRNA with the sequences of other small-subunit rRNAs

We present the sequence of the nuclear-encoded ribosomal small-subunit RNA from soybean. The soybean 18S rRNA sequence of 1807 nucleotides (nt) is contained in a gene family of approximately 800 closely related members per haploid genome. This sequence is compared with the ribosomal small-subunit RNAs of maize (1805 nt), yeast (1789 nt), Xenopus (1825 nt), rat (1869 nt), and Escherichia coli (1541 nt). Significant sequence homology is observed among the eukaryotic small-subunit rRNAs examined, and some sequence homology is observed between eukaryotic and prokaryotic small-subunit rRNAs. Conserved regions are found to be interspersed among highly diverged sequences. The significance of these comparisons is evaluated using computer simulation of a random sequence model. A tentative model of the secondary structure of soybean 18S rRNA is presented and discussed in the context of the functions of the various conserved regions within the sequence. On the basis of this model, the short base-paired sequences defining the four structural and functional domains of all 18S rRNAs are seen to be well conserved. The potential roles of other conserved soybean 18S rRNA sequences in protein synthesis are discussed.

Similar amino acid sequences: chance or common ancestry?

The systemic comparison of every newly determined amino acid sequence with all other known sequences may allow a complete reconstruction of the evolutionary events leading to contemporary proteins. But sometimes the surviving similarities are so vague that even computer-based sequence comparisons procedures are unable to validate relationships. In other cases similar sequences may appear in totally alien proteins as a result of mere chance or, occasionally, by the convergent evolution of sequences with special properties.