The present disclosure is directed to enabling search and extraction of data stored in DNA with optimized data structures and functions. Accordingly, systems and methods are provided herein for performing certain functions on data stored in nucleic acid molecules. The present disclosure covers at least the following areas of interest: (1) data structures to provide efficient access and search of information stored in nucleic acid molecules, (2) accurate and quick reading of information stored in nucleic acid molecules, (3) targeted approaches to accessing subsets of information stored in nucleic acid molecules, (4) a rank function that determines a count of particular bit or symbol value in a set of information stored in nucleic acid molecules, (5) functions including counting, locating, and extracting occurrences of a specific pattern in a message of information stored in nucleic acid molecules, and (6) an if-then-else operation to sort data stored in nucleic acid molecules.

The present invention is related to the field of bio/chemical sampling, sensing, assays and applications. Particularly, the present invention is related to how to make the sampling/sensing/assay become simple to use, fast to results, highly sensitive, easy to use, using tiny sample volume (e.g., 0.5 μL or less), operated by a person without any professionals, reading by mobile-phone, or low cost, or a combination of them.

The present invention is related to the field of bio/chemical sampling, sensing, assays and applications. Particularly, the present invention is related to how to make the sampling/sensing/assay become simple to use, fast to results, highly sensitive, easy to use, using tiny sample volume (e.g., 0.5 μL or less), operated by a person without any professionals, reading by mobile-phone, or low cost, or a combination of them.

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.

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.

Trusted, privacy-protected systems and methods are disclosed for processing, handling, and performing tests on human genomic and other information. According to some embodiments, a system is disclosed that is a cloud-based system for the trusted storage and analysis of genetic and other information. Some embodiments of the system may include or support some or all of authenticated and certified data sources; authenticated and certified diagnostic tests; and policy-based access to data.

Trusted, privacy-protected systems and methods are disclosed for processing, handling, and performing tests on human genomic and other information. According to some embodiments, a system is disclosed that is a cloud-based system for the trusted storage and analysis of genetic and other information. Some embodiments of the system may include or support some or all of authenticated and certified data sources; authenticated and certified diagnostic tests; and policy-based access to data.

A biometric biochemical analysis system includes a user interface module to provide instructions for collecting and handling biochemical sampling and processing related to biometric data gathering as well as capturing biometric data using digital data capturing devices. The user interface module and display are integrated with analysis and communications portions of the biometric biochemical analysis system to provide a portable system for multi-portion data collecting, storage, verification, and analysis.

A biometric biochemical analysis system includes a user interface module to provide instructions for collecting and handling biochemical sampling and processing related to biometric data gathering as well as capturing biometric data using digital data capturing devices. The user interface module and display are integrated with analysis and communications portions of the biometric biochemical analysis system to provide a portable system for multi-portion data collecting, storage, verification, and analysis.

An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.