Algorithm of OMA for large-scale orthology inference Export

@article{Roth2008, abstract = {BACKGROUND:OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind.RESULTS:The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests.CONCLUSION:OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments.}, author = {Roth, Alexander and Gonnet, Gaston and Dessimoz, Christophe}, citeulike-article-id = {3747527}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-518}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19055798}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19055798}, day = {04}, doi = {10.1186/1471-2105-9-518}, issn = {1471-2105}, journal = {BMC Bioinformatics}, month = {December}, number = {1}, pages = {518+}, posted-at = {2009-01-19 09:08:23}, priority = {3}, title = {Algorithm of OMA for large-scale orthology inference}, url = {http://dx.doi.org/10.1186/1471-2105-9-518}, volume = {9}, year = {2008} }

TY - JOUR ID - Roth2008 L3 - citeulike-article-id:3747527 N2 - BACKGROUND:OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind.RESULTS:The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests.CONCLUSION:OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Algorithm of OMA for large-scale orthology inference VL - 9 SP - 518 AU - Roth, Alexander AU - Gonnet, Gaston AU - Dessimoz, Christophe PY - 2008/12/04/ UR - http://dx.doi.org/10.1186/1471-2105-9-518 ER -