Daniel B. Davison

A Measure of DNA Sequence Dissimilarity Based on Mahalanobis Distance Between Frequencies of Words

A number of algorithms exist for searching genetic databases for biologically significant similarities in DNA sequences. Past research has shown that word-based search tools are computationally efficient and can find similarities or dissimilarities invisible to other algorithms like FASTA. We characterize a family of word-based dissimilarity measures that define distance between two sequences by simultaneously comparing the frequencies of all subsequences of n adjacent letters (i.e., n-words) in the two sequences. Applications to real data demonstrate that currently used word-based methods that rely on Euclidean distance can be significantly improved by using Mahalanobis distance, which accounts for both variances and covariances between frequencies of n-words. Furthermore, in those cases where Mahalanobis distance may be too difficult to compute, using standardized Euclidean distance, which only corrects for the variances of frequencies of n-words, still gives better performance than the Euclidean distance. Also, a simple way of combining distances obtained at different n-words is considered. The goal is to obtain a single measure of dissimilarity between two DNA sequences. The performance ranking of the preceding three distances still holds for their combined counterparts. All results obtained in this paper are applicable to amino acid sequences with minor modifications.

Antimicrobial resistance - why do we have it and what can we do about it?

An accurate, but oversimplified statement is that the time of use of antimicrobial agents has ‘built-in obsolescence’ due to resistance emergence. Every antimicrobial agent in use loses efficacy as a result of either genetic acquisition of resistance encoding genes or by mutation of the target(s) of the drug. The end result is the survival and proliferation of the resistant organism. The powerful selection imposed by antimicrobial agents eliminates the susceptible organism, thereby enlarging the niche for the surviving, resistant forms to proliferate. At one point, it was believed that the resistant organisms might be at a selective disadvantage (due to the genetic load imposed by the resistance genes) relative to susceptible strains in the absence of antibiotic challenge. This suggested that cycling of antibiotic use might be an effective strategy to combat resistance to classes of antibiotics. Recent studies on the role of intragenic and extragenic compensatory mutations in increasing the fitness of resistant mutants have stripped us of this simple notion and makes the picture all the more complex and dismal.

Binformatics: not just for sequences anymore

Abstract The expansion of genomic information has made data integration as important to bioinformatics as computational analyses. A ‘systems biology’ approach to understanding drug targets requires integrating diverse types of data, including nucleotide and protein sequences, mRNA and protein expression measurements, model organism data, alternative splicing, single nucleotide polymorphisms (SNPs) and more. This review describes how publicly available databases and data formats facilitate such integration. However, this discussion is by no means comprehensive. It represents the tools and approaches that Bristol-Myers Squibb (BMS) Bioinformatics has chosen to pursue. At BMS, two tools provide access to this information. Genome browsers provide graphic overviews of sequence-based information, whereas a curated database of drug target information provides annotation and analyses. The integration of all these functions results in a flexible bioinformatics infrastructure for drug discovery.

An overview of sequence similarity ("homology") searching.

Sequence similarity searching is an essential tool for molecular biologists. It is used to support inference of protein function and for phylogenetic analysis. Every searching procedure requires some understanding of the underlying principles, so at the very least the investigators selection of parameters is correct. The principles underlying the most commonly used procedures are presented in this unit. The discussion is intentionally non‐mathematical, but does contain references for those who desire a mathematical and statistical discussion of these procedures.