The home of algorithmic bioinformatics research and its application to real-world data, Algorithms for Molecular Biology encompasses articles about novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, combinatorial algorithms, and machine learning. With an expert international Editorial Board providing fast and rigorous peer review, the open access journal also welcomes pure algorithm papers and software articles about novel tools, where future applications to biological data are to be expected, or where they address unique computational problems in the world of molecular biology.
Algorithms for Molecular Biology
Description
Identifiers
e-ISSN | 1748-7188 |
Publisher
BioMed Central
Additional information
Data set: Springer
Articles
Algorithms for Molecular Biology > 2019 > 14 > 1 > 1-15
Background We study a preprocessing routine relevant in pan-genomic analyses: consider a set of aligned haplotype sequences of complete human chromosomes. Due to the enormous size of such data, one would like to represent this input set with a few founder sequences that retain as well as possible the contiguities of the original sequences. Such a smaller set gives a scalable way to exploit pan-genomic...
Algorithms for Molecular Biology > 2019 > 14 > 1 > 1-14
This paper generalizes previous studies on genome rearrangement under biological constraints, using double cut and join (DCJ). We propose a model for weighted DCJ, along with a family of optimization problems called $$\varphi$$ φ -MCPS (Minimum Cost Parsimonious Scenario), that are based on labeled graphs. We show how to compute solutions to general instances of $$\varphi$$ φ -MCPS, given an algorithm...
Algorithms for Molecular Biology > 2019 > 14 > 1 > 1-17
Background Divide-and-conquer methods, which divide the species set into overlapping subsets, construct a tree on each subset, and then combine the subset trees using a supertree method, provide a key algorithmic framework for boosting the scalability of phylogeny estimation methods to large datasets. Yet the use of supertree methods, which typically attempt to solve NP-hard optimization problems,...