Provided are a test order processing apparatus capable of communicating with a test apparatus and a test system using same. The test order processing apparatus contains a test order receiving part that receives a test order containing plural test items, a linking information-retaining part that forms linking information for linking an individual test item to a test apparatus appropriate therefor, according to the linking information, a test request sending part that sends each test item to a test apparatus appropriate therefor, a test result receiving part that receives the test results sent back from each test apparatus and forms a test report, and a test report sending part that sends the test report to a sender of the test order.

Disclosed herein are methods, systems, platforms, non-transitory computer-readable medium, services, and kits for determining a cancer type in an individual. Also described herein include methods, systems, platforms, non-transitory computer-readable medium, and compositions for generating a CpG methylation profile database.

Embodiments of a method and/or system for characterizing one or more appendix-related conditions can include determining a microorganism dataset associated with a set of subjects; and/or performing a characterization process associated with the one or more appendix-related conditions, based on the microorganism dataset, where performing the characterization process can additionally or alternatively include performing an appendix-related characterization process for the one or more appendix-related conditions, and/or determining one or more therapies.

A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.

The invention provides methods for identifying rare variants near a structural variation in a genetic sequence, for example, in a nucleic acid sample taken from a subject. The invention additionally includes methods for aligning reads (e.g., nucleic acid reads) to a reference sequence construct accounting for the structural variation, methods for building a reference sequence construct accounting for the structural variation or the structural variation and the rare variant, and systems that use the alignment methods to identify rare variants. The method is scalable, and can be used to align millions of reads to a construct thousands of bases long, or longer.

The present invention relates to a method for calibrating a data set of a target analyte in a sample, wherein a normalization coefficient for calibrating the data set is provided by using a reference value, a reference cycle and the data set, and the calibrated data set is obtained by applying the normalization coefficient to the signal values of the data set. The present method is very effective in removing the inter- and intra-instrument signal variations of data sets. Furthermore, since the present method can be configured in software, the instant method is capable of being applied universally to various analytical instruments (e.g., a real-time PCR instrument) regardless of manufacturer. Accordingly, the method by the present invention would be very useful in diagnostic data analysis.

Methods are provided of using a database that stores a plurality of reference genomes and phylogenetic information which relates the stored reference genomes to each other in a phylogenetic structure. These methods are useful in analysing the bacteria and/or bacterial lineages present in a sample and to identify a bacterium for use in therapy.

The components and structure for a genome created for the purpose of the evolutionary development of artificial intelligence systems/machines without human intervention.

There is provided a method of computing computed pathological indication(s), comprising: receiving an indication of values of current laboratory test results calculated based on an automated analysis of laboratory sample(s) collected from a target individual, selecting classifier(s) according to an analysis of the indication of values of the current laboratory test results, determining additional laboratory test(s) according to the analysis of the indication of values of the current laboratory test results and/or the selected classifier(s), generating instructions for automatic execution of second automatic laboratory testing device(s) on the laboratory sample(s) to obtain a second indication of a second value of the additional laboratory test(s), and evaluating computed pathological indication(s) by applying the selected classifier(s) to the indication of values of the current laboratory test results and the second indication of the second value of the additional laboratory test result(s).

Techniques for identifying regions in nucleic acid sequences for which to design highly discriminatory primers are provided. In some embodiments, a corpus of nucleic acid sequences may be divided into a first set and a second set, and a respective index may be built containing data structures representing a plurality of k-mers of each nucleic acid sequence. By comparing the data structures of the first index to one another, a system may iteratively determine whether each k-mer over a given region in one of the nucleic acid sequences in the first set are also found in every other sequence in the first set. By comparing against the data structures in the second index, a system may then iteratively determine whether all k-mers in the region can be found in the same order of in any of the nucleic acid sequences in the second set.