Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning.
Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms.
- Burkhard Morgenstern, University of Göttingen
- Peter Stadler, University of Leipzig
Prof. Desmond G Higgins
Prof Des Higgins has been working in the areas of bioinformatics and molecular evolution since 1985, predominantly on methods and software for DNA and protein sequence alignment. He first began developing solutions to the impracticality of sequence alignment while at Trinity College, Dublin, where he wrote a series of programs, called Clustal, which, have become the most widely used software for such analysis. Prof Higgins has continued to work on Clustal, developing new programs and improved interfaces. He now works in University College, Dublin where his group works on developing new bioinformatics and statistical tools for evolutionary biologists, the application of multivariate analysis of "omics" data, and addresses molecular evolutionary questions using bioinformatics approaches.
Prof Higgins co-authored the article 'Sequence embedding for fast construction of guide trees for multiple sequence alignment' in Algorithms for Molecular Biology in May 2010. The article shows how embedding methods can be a quick and effective way of sequence clustering prior to multiple sequence alignment analysis.
Dr Irmtraud M. Meyer
Dr Irmtraud Meyer has been working in the field of bioinformatics and sequence analysis since her PHD under Dr Richard Durbin at the Sanger Institute in Cambridge, UK. Dr Meyer has since worked at the European Bioinformatics Institute, and the Oxford Centre for Gene Function, University of Oxford. Dr Meyer is currently Assistant Professor at the University of Columbia, where her research includes NA structure and function prediction, prediction of protein-coding genes and RNA genes and novel algorithms and computational methods.
Dr Meyer co-authored ‘Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training’, published in Algorithms for Molecular Biology in December 2010. The article introduces two new algorithms to optimise the performance of hidden Markov models in bioinformatics applications.