First and second sequenced outputs are accessed. The sequenced outputs contain variants occurring at different carriers and at different carrier positions. Hashes are generated over a selected pattern length of positions for those carrier positions that are shared between the sequenced outputs to produce window hashes for base patterns in first and second sequences. Each sequence is based on the shared carrier positions and the respective sequenced output. The window hashes are non-unique. Window hashes that occur less than a ceiling number times are selected. The selected window hashes are compared between the sequences on a starting position basis such that selected window hashes for base patterns having same start positions in the sequenced outputs are compared. Common window hashes are identified between the sequences based on the comparing. A similarity measure is determined between the sequences based on the common window hashes.

Embodiments of the present disclosure provide a method and Internet of Things system for deploying nucleic acid detection points in a smart city. This method is executed based on a management platform of the Internet of Things system for deploying nucleic acid detection points in a smart city, comprising: predicting nucleic acid detection person-time in a preset future period in at least one area in multiple areas based on epidemic information and environmental information in multiple areas; determining a deployment plan of the nucleic acid detection points based on the predicted nucleic acid detection person-time.

Embodiments of the present disclosure provide a method and Internet of Things system for deploying nucleic acid detection points in a smart city. This method is executed based on a management platform of the Internet of Things system for deploying nucleic acid detection points in a smart city, comprising: predicting nucleic acid detection person-time in a preset future period in at least one area in multiple areas based on epidemic information and environmental information in multiple areas; determining a deployment plan of the nucleic acid detection points based on the predicted nucleic acid detection person-time.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

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.