Methods and systems are disclosed for encoding and decoding data from genetic traits. In one embodiment, the invention provides a method of encoding data from genetic traits. The method comprises encoding genetic traits information, including using quantum dot wavelengths to identify distinct genetic traits, and using numbers of the quantum dots to represent probabilities associated with the traits. In an embodiment, the invention provides a genetic characteristics decoding system for decoding genetic information encoded using quantum dots in a carrier. The decoding system comprises a light source for charging the quantum dots in the carrier; a scanner for scanning the carrier to retrieve information from the charged quantum dots; and a processing system for processing the retrieved information to determine quantum dot wavelengths to identify distinct genetic traits, and to determine numbers of the quantum dots to identify probabilities associated with the genetic traits.

Genomics information such as DNA, RNA and proteins carry a wealth of sensitive information, the exposure of which risks compromising the privacy and/or business interest of individuals and companies. An apparatus, a system and methods are disclosed for protecting sensitive genomic information either as it is produced by a sequencing machine or immediately therafter, then throughout the whole genomic workflow. Raw genomic data (“reads”) is detected and classified according to sensitivity. Reads are decomposed by excising the number and type of detected sensitive base or base pairs in less sensitive or insensitive parts of the read. The genomic workflow processes the excised information locally or in a distributed fashion, preferably within trusted execution environments for increased security.

Genomics information such as DNA, RNA and proteins carry a wealth of sensitive information, the exposure of which risks compromising the privacy and/or business interest of individuals and companies. An apparatus, a system and methods are disclosed for protecting sensitive genomic information either as it is produced by a sequencing machine or immediately therafter, then throughout the whole genomic workflow. Raw genomic data (“reads”) is detected and classified according to sensitivity. Reads are decomposed by excising the number and type of detected sensitive base or base pairs in less sensitive or insensitive parts of the read. The genomic workflow processes the excised information locally or in a distributed fashion, preferably within trusted execution environments for increased security.

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 fluorescent in-situ hybridization imaging and analysis system includes a flow cell to contain a sample to be exposed to fluorescent probes in a reagent, a fluorescence microscope to obtain sequentially collect a plurality of images of the sample at a plurality of different combinations of imaging parameters, and a data processing system. The data processing system includes an online pre-processing system configured to sequentially receive the images from the fluorescence microscope as the images are collected and perform on-the-fly image pre-processing to remove experimental artifacts of the image and to provide RNA image spot sharpening, and an offline processing system configured to, after the plurality of images are collected, perform registration of images having a same field of view and to decode intensity values in the plurality of images to identify expressed genes.

A fluorescent in-situ hybridization imaging and analysis system includes a flow cell to contain a sample to be exposed to fluorescent probes in a reagent, a fluorescence microscope to obtain sequentially collect a plurality of images of the sample at a plurality of different combinations of imaging parameters, and a data processing system. The data processing system includes an online pre-processing system configured to sequentially receive the images from the fluorescence microscope as the images are collected and perform on-the-fly image pre-processing to remove experimental artifacts of the image and to provide RNA image spot sharpening, and an offline processing system configured to, after the plurality of images are collected, perform registration of images having a same field of view and to decode intensity values in the plurality of images to identify expressed genes.

The techniques and/or systems described herein are directed to improvements in genomic prediction using homomorphic encryption. For example, a genomic model can be generated by a prediction service provider to predict a risk of a disease or a presence of genetic traits. Genomic data corresponding to a genetic profile of an individual can be batch encoded into a plurality of polynomials, homomorphically encrypted, and provided to a service provider for evaluation. The genomic model can be batch encoded as well, and the genetic prediction may be determined by evaluating a dot product of the genomic model data the genomic data. A genomic prediction result value can be provided to a computing device associated with a user for subsequent decrypting and decoding. Homomorphic encoding and encryption can be used such that the genomic data may be applied to the prediction model and a result can be obtained without revealing any information about the model, the genomic data, or any genomic prediction.

Techniques for securely encoding, communicating, and comparing genomic information using probabilistic data structures are provided. In some embodiments, genomic information in a secure computing environment may be encoded and/or anonymized by building a probabilistic data structure that represents sub-strings of the genomic information as members of a set; the probabilistic data structure may then be securely transmitted outside the secure computing environment. In some embodiments, a probabilistic data structure representing sub-strings of sensitive genomic information as members of a set may be received in an unsecure computing environment and may be queried to generate output data indicating whether reference sub-strings are probable members of the set. In some embodiments, querying the probabilistic data structure, and other techniques of analyzing the probabilistic data structure, may be used to determine whether the sensitive genomic information corresponds to an organism associated with the reference genomic information.

Techniques for securely encoding, communicating, and comparing genomic information using probabilistic data structures are provided. In some embodiments, genomic information in a secure computing environment may be encoded and/or anonymized by building a probabilistic data structure that represents sub-strings of the genomic information as members of a set; the probabilistic data structure may then be securely transmitted outside the secure computing environment. In some embodiments, a probabilistic data structure representing sub-strings of sensitive genomic information as members of a set may be received in an unsecure computing environment and may be queried to generate output data indicating whether reference sub-strings are probable members of the set. In some embodiments, querying the probabilistic data structure, and other techniques of analyzing the probabilistic data structure, may be used to determine whether the sensitive genomic information corresponds to an organism associated with the reference genomic information.