2018/08/23 by David A. Morrison, Morrison, David A. · 1 voice
Biochemistry, Genetics and Molecular Biology · #Genomics and Phylogenetic Studies #Glycosylation and Glycoproteins Research #Machine Learning in Bioinformatics #q-bio.PE
paper · pdf · doi:10.48550/arxiv.1808.07717
openalex publication_date 2018/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multiple sequence alignment is a basic procedure in molecular biology, and it is often treated as being essentially a solved computational problem. However, this is not so, and here I review the evidence for this claim, and outline the requirements for a solution. The goal of alignment is often stated to be to juxtapose nucleotides (or their derivatives, such as amino acids) that have been inherited from a common ancestral nucleotide (although other goals are also possible). Unfortunately, this is not an operational definition, because homology (in this sense) refers to unique and unobservable historical events, and so there can be no objective mathematical function to optimize. Consequently, almost all algorithms developed for multiple sequence alignment are based on optimizing some sort of compositional similarity (similarity = homology + analogy). As a result, many, if not most, practitioners either manually modify computer-produced alignments or they perform de novo manual alignment, especially in the field of phylogenetics. So, if homology is the goal, then multiple sequence alignment is not yet a solved computational problem. Several criteria have been developed by biologists to help them identify potential homologies (compositional, ontogenetic, topographical and functional similarity, plus conjunction and congruence), and these criteria can be applied to molecular data, in principle. Current computer programs do implement one (or occasionally two) of these criteria, but no program implements them all. What is needed is a program that evaluates all of the evidence for the sequence homologies, optimizes their combination, and thus produces the best hypotheses of homology. This is basically an inference problem not an optimization problem.