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Cheminformatics Benchmark Toolkit --------------------------------- Why? Because we want to: 1. measure if a patch improves performance 2. compare cheminformatics toolkits Project ------- This is an open project, and anyone can contribute or patch the tests, in order to attempt to create a balanced and fair test suite impartial to the toolkit being tested. Versioning ---------- When reporting results for a toolkit for a test, always report the version of ChemInfBenchmark release, as well as the test number as listed below. The tests --------- Toolkits should always to the bare minimum; any extra things will only make the outcome unfavourable, but toolkits may do that if they wish. 1. Convert an SD file to XYZ 2. Calculate the molecular mass of the entries in an SD file 3. Convert an SD file to CML 2.5 4. Perform a substructure match on all of the entries in an SD file 5. Parse a collection of SMILES - this will be a measure of performance and completeness 6. Detect rings in a collection of molecules. 7. Parse (not perform the search) for a collection of SMARTS patterns - this will be a measure of performance and completeness Running tests ------------- Each test is run via a configure file. A test can be run by $ ant -Dconfig=test-config.xml run

A tool for visualizing abnormal read pairs (i.e. too far apart, forward-forward or reverse-reverse). One of the ways to identify structural variation in a genome is to look at mapping of a read-pair library on a reference genome. Any read-pair (i.e. the ends of the same clone) normally maps in a forward-reverse orientation at a distance from each other that corresponds to the insert size of the clone library. Structural variation between the reference genome used for mapping and the genome used to create the clone library will cause the reads to map in the wrong orientation (i.e. forward-forward or reverse-reverse; indicating an inversion) or at too large or too small a distance (indicating an insertion or deletion in the sample, respectively). pARP basically displays raw read pair mapping data and does not make any assumptions on models as might be required when doing a statistical analysis of the same data. By showing a birds' eye view it helps in interpreting what structural variations are present.

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