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GraphQL is a data query and manipulation language that allows specifying what data is to be retrieved ("declarative data fetching") or modified. A GraphQL server can process a client query using data from separate sources and present the results in a unified graph . [ 2 ]
This query would return the city of residence of each person in the graph with residential information, and, if an EU national, which country they come from. Queries are therefore able to first project a sub-graph of the graph input into the query, and then extract the data values associated with that subgraph.
These steps are sufficient to provide general purpose computing and what is typically required to express the common motifs of any graph traversal query. Given that Gremlin is a language, an instruction set, and a virtual machine, it is possible to design another traversal language that compiles to the Gremlin traversal machine (analogous to ...
Weak mutation testing (or weak mutation coverage) requires that only the first and second conditions are satisfied. Strong mutation testing requires that all three conditions are satisfied. Strong mutation is more powerful, since it ensures that the test suite can really catch the problems. Weak mutation is closely related to code coverage ...
The test output explains if the alteration is a known or predicted harmless or disease-causing mutation and gives detailed information about the mutation. Importantly, the predictions of clinical effects of mutations suffer from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, including ...
A GraphML file consists of an XML file containing a graph element, within which is an unordered sequence of node and edge elements. Each node element should have a distinct id attribute, and each edge element has source and target attributes that identify the endpoints of an edge by having the same value as the id attributes of those endpoints.
In software architecture, publish–subscribe or pub/sub is a messaging pattern where publishers categorize messages into classes that are received by subscribers. This is contrasted to the typical messaging pattern model where publishers send messages directly to subscribers.
High sensitivity and specificity, using base qualities at all steps in the alignment. Includes adapter trimming, base quality calibration, Bi-Seq alignment, and options for reporting multiple alignments per read. Use of ambiguous IUPAC codes in reference for common SNPs can improve SNP recall and remove allelic bias. Yes Yes Yes