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MySQL Error: 1194 (Table 'pwn_comment' is marked as crashed and should be repaired)
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网友点评-Ses of this ontology {they can|they are able to|they-线缆测高仪,超声波测高仪, 手持式测高仪-上海交通大学科技园上海野豹企业发展公司
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发布于:2018-1-25 12:39:22  访问:41 次 回复: 篇
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Ses of this ontology {they can|they are able to|they
If a venom component is usually a peptide, it includes a canonical amino acid sequence. Each and every of your entities may have a single or extra other pieces of metadata, which includes hyperlinks to other ontologies and structured terminologies. Right after defining the class structure from the ontology, we populated the ontology with men and women (precise instances of your ontology‘s classes) sourced from UniProtKB/Swiss-Prot‘s Tox-Prot database [12]. This database is usually a manually curated list of venom peptides containing numerous annotation tags like species of origin, amino acid sequences, complete taxonomic lineage, and automated cross-mappings to other on the net sources. On the other hand, the structure of Tox-Prot will not support semantic reasoning. Because of the massive number of individual records in the Tox-Prot database (six,092 in the time of developing the ontology), we added the contained data programmatically by first exporting the ontology from Prot to an RDF-formatted XML file [13], and after that utilizing Apache‘s Jena framework [14] to parse the venom records and insert relevant information in to the proper spot within the ontology‘s class hierarchy. two.two Exploratory evaluation of Venom Ontology information records To demonstrate some possible applications of Venom Ontology, we performed 3 exploratory analyses of its contained data. The first of those involved assessing the similarity of amino acid sequences for venom peptides developed by species from the very same genera. To accomplish this, we grouped species (stored as "Organisms" inside the ontology) by genus, along with their derived peptide compounds. We then chosen 2 genera which can be effectively represented within the information set, and constructed "sequence similarity networks" for each of them. In deciding on these genera, we looked for ones that happen to be prolific enough within the ontology to generate informative (non-trivial) networks, however not so prolific as to become unwieldy in terms of visualization or computation. In practice, we looked for two genera with approximately 20 species in the ontology. For each and every genus chosen, we used BLASTp [15] to align all pairs of peptides within the genus. We constructed the networks working with peptide sequences as nodes, plus the alignments in between them as edges. We transformed the BLAST scores (which represent the percent coverage from the alignments; denoted !) for alignments applying the following equation: !" which makes it possible for us to define a "distance" amongst two peptide sequences (i.e., smaller sized values of !‘ indicate larger similarity), utilised as edge weights inside the final networks. !‘ is often a value within the interval [0,1), and is OPC-67683 dose normally pretty smaller (e.g., 110-15). Ultimately, we filtered edges by setting a maximum anticipate value ("e-value" a normalized p-value defining self-assurance that the alignment is non-random) threshold of 110-50. Alignments that fell below this maximum cutoff nearly absolutely signify evolutionarily connected sequences, and are therefore informative for the purposes of constructing these networks. For visualization purposes, we rendered the networks in Cytoscape [16] applying the Prefuse force-directed layout [17], and colored nodes (person peptides) by the species from which these peptides were sourced. Our second evaluation was a standard exploration on the distribution of each species and individual peptides in the ontology across the tree of life.
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