title: Orthology detection combining clustering and synteny for very large datasets.
abstract: The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.
authors.affiliation: Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marburg, Germany.
authors.forename: Marcus
authors.initials: M
authors.lastname: Lechner
authors.affiliation: Bioinformatics Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany; Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany; Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Brasília, Brasil.
authors.forename: Maribel
authors.initials: M
authors.lastname: Hernandez-Rosales
authors.affiliation: Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany; Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Doerr
authors.affiliation: Faculty of Mathematics and Computer Science University of Leipzig, Leipzig, Germany.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Wieseke
authors.affiliation: Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany; Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
authors.forename: Annelyse
authors.initials: A
authors.lastname: Thévenin
authors.affiliation: Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany; Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
authors.forename: Jens
authors.initials: J
authors.lastname: Stoye
authors.affiliation: Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marburg, Germany.
authors.forename: Roland K
authors.initials: RK
authors.lastname: Hartmann
authors.affiliation: Computational EvoDevo Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany.
authors.forename: Sonja J
authors.initials: SJ
authors.lastname: Prohaska
authors.affiliation: Bioinformatics Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany; Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany; Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria; Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark; The Santa Fe Institute, Santa Fe, New Mexico, United States of America; RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Leipzig, Germany.
authors.forename: Peter F
authors.initials: PF
authors.lastname: Stadler
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics; D016000:Cluster Analysis; D003198:Computer Simulation; D066264:Datasets as Topic; D005798:Genes, Bacterial; D008957:Models, Genetic; D012984:Software; D026801:Synteny
chemical_list: D001426:Bacterial Proteins