Titel
Orthology Detection Combining Clustering and Synteny for Very Large Datasets
Autor*in
Marcus Lechner
Institut für Pharmazeutische Chemie, Philipps-Universität Marburg
Maribel Hernandez-Rosales
Bioinformatics Group, Department of Computer Science, Universität Leipzig
Daniel Doerr
Genome Informatics, Faculty of Technology, Bielefeld University
... show all
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.
Stichwort
Genome analysisPhylogeneticsPhylogenetic analysisPlant genomicsGenomic databasesSimulation and modelingEvolutionary geneticsChromosomes
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
Erschienen in
Titel
PLoS ONE
Band
9
Ausgabe
8
Publication
Public Library of Science (PLoS)
Erscheinungsdatum
2014
Zugänglichkeit
Rechte
Universität Wien | Universitätsring 1 | 1010 Wien | T
+43-1-4277-0