Algorithms for Molecular Biology publishes articles on novel and improved algorithms and methods in bioinformatics. 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, combinatorial algorithms, statistical methods, machine learning, and network analysis. Where appropriate, manuscripts should also describe applications to real-world data and/or evaluations of algorithms on simulated data. Pure algorithm papers are welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools and benchmarking studies will be considered for publication if they contain algorithmically interesting aspects. The journal does not publish applications of established tools, methods, or workflows to particular biological case studies and descriptions of incremental improvements of software tools or data resources. .