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Cymbidium goeringii, commonly known as the spring orchid, has long been favoured for horticultural purposes in Asian countries. It is a popular orchid with much demand for improvement and development for its valuable varieties. Until now, its reference genome has not been published despite its popularity and conservation efforts. Here, we report the de novo assembly of the C. goeringii genome, which is the largest among the orchids published to date, using a strategy that combines short‐ and long‐read sequencing and chromosome conformation capture (Hi‐C) information. The total length of all scaffolds is 3.99 Gb, with an N50 scaffold size of 178.2 Mb. A total of 29,556 protein‐coding genes were annotated and 3.55 Gb (88.87% of genome) repetitive sequences were identified. We constructed pseudomolecular chromosomes using Hi‐C, incorporating 89.4% of the scaffolds in 20 chromosomes. We identified 220 expanded and 106 contracted genes families in C. goeringii after divergence from its close relative. We also identified new gene families, resistance gene analogues and changes within the MADS‐box genes, which control a diverse set of developmental processes during orchid evolution. Our high quality chromosomal‐level assembly of C. goeringii can provide a platform for elucidating the genomic evolution of orchids, mining functional genes for agronomic traits and for developing molecular markers for accelerated breeding as well as accelerating conservation efforts. [ABSTRACT FROM AUTHOR]

Abstract Background Thoroughbred horses are the most expensive domestic animals, and their running ability and knowledge about their muscle-related diseases are important in animal genetics. While the horse reference genome is available, there has been no large-scale functional annotation of the genome using expressed genes derived from transcriptomes. Results We present a large-scale analysis of whole transcriptome data. We sequenced the whole mRNA from the blood and muscle tissues of six thoroughbred horses before and after exercise. By comparing current genome annotations, we identified 32,361 unigene clusters spanning 51.83 Mb that contained 11,933 (36.87%) annotated genes. More than 60% (20,428) of the unigene clusters did not match any current equine gene model. We also identified 189,973 single nucleotide variations (SNVs) from the sequences aligned against the horse reference genome. Most SNVs (171,558 SNVs; 90.31%) were novel when compared with over 1.1 million equine SNPs from two SNP databases. Using differential expression analysis, we further identified a number of exercise-regulated genes: 62 up-regulated and 80 down-regulated genes in the blood, and 878 up-regulated and 285 down-regulated genes in the muscle. Six of 28 previously-known exercise-related genes were over-expressed in the muscle after exercise. Among the differentially expressed genes, there were 91 transcription factor-encoding genes, which included 56 functionally unknown transcription factor candidates that are probably associated with an early regulatory exercise mechanism. In addition, we found interesting RNA expression patterns where different alternative splicing forms of the same gene showed reversed expressions before and after exercising. Conclusion The first sequencing-based horse transcriptome data, extensive analyses results, deferentially expressed genes before and after exercise, and candidate genes that are related to the exercise are provided in this study.

Abstract Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets. Conclusion We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.

Abstract Background Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of Xanthomonas oryzae pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways. Description A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome. Conclusion XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from http://bioportal.kobic.kr/XooNET/.

Abstract Background Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. Description We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. Conclusion InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.