Thu, 21 Aug 2008 15:33:56 BST CiteULike: neils Kelley http://www.citeulike.org/user/neils/author/Kelley CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/neils/article/2743652 <i>BMC Genomics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Xanthomonas oryzae pv. oryzae causes bacterial blight of rice, a major disease that constrains production of this staple crop in many parts of the world. We report here on the complete genome sequence of strain PXO99A and its comparison to two previously sequenced strains, KACC10331 and MAFF311018, which are highly similar to one another.RESULTS:The PXO99A genome is a single circular chromosome of 5,240,075 bp, considerably longer than the genomes of the other strains (4,941,439 bp and 4,940,217 bp, respectively), and it contains 5083 protein-coding genes, including 87 not found in KACC10331 or MAFF311018. PXO99A contains a greater number of virulence-associated transcription activatoralike effector genes and has at least ten major chromosomal rearrangements relative to KACC10331 and MAFF311018. PXO99A contains numerous copies of diverse insertion sequence elements, members of which are associated with 7 out of 10 of the major rearrangements. A rapidly-evolving CRISPR (clustered regularly interspersed short palindromic repeats) region contains evidence of dozens of phage infections unique to the PXO99A lineage. PXO99A also contains a unique, near-perfect tandem repeat of 212 kilobases close to the replication terminus.CONCLUSIONS:Our results provide striking evidence of genome plasticity and rapid evolution within Xanthomonas oryzae pv. oryzae. The comparisons point to sources of genomic variation and candidates for strain-specific adaptations of this pathogen that help to explain the extraordinary diversity of Xanthomonas oryzae pv. oryzae genotypes and races that have been isolated from around the world. Genome sequence and rapid evolution of the rice pathogen Xanthomonas oryzae pv. oryzae PXO99A Steven Salzberg Daniel Sommer Michael Schatz Adam Phillippy Pablo Rabinowicz Seiji Tsuge Ayako Furutani Hirokazu Ochiai Arthur Delcher David Kelley Ramana Madupu Daniela Puiu Diana Radune Martin Shumway Cole Trapnell Gudlur Aparna Gopaljee Jha Alok Pandey Prabhu Patil Hiromichi Ishihara Damien Meyer Boris Szurek Valerie Verdier Ralf Koebnik Maxwell Dow Robert Ryan Hisae Hirata Shinji Tsuyumu Sang Lee Pamela Ronald Ramesh Sonti Marie Van Sluys Jan Leach Frank White Adam Bogdanove doi:10.1186/1471-2164-9-204 BMC Genomics, Vol. 9, No. 1. (2008) 2008-05-01T23:28:04-00:00 2008 BMC Genomics 9 1 evolution genome pathogen rice sequence xanthomonas http://www.citeulike.org/user/neils/article/2693993 <i>PLoS Comput Biol, Vol. 4, No. 4. (Apr 2008)</i><br /><br />Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function. Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data Sourav Bandyopadhyay Ryan Kelley Nevan Krogan Trey Ideker doi:10.1371/journal.pcbi.1000065 PLoS Comput Biol, Vol. 4, No. 4. (Apr 2008) 2008-04-21T02:13:52-00:00 2008 PLoS Comput Biol 4 4 Public Library of Science bioinformatics complex genetics interaction network prediction protein-protein