Embodiments of the present invention may provide the capability to predict the metastasis of cancer in a patient from one tissue to another. In an embodiment, a computer-implemented method for predicting metastasis may comprise receiving an indication of at least one disrupted gene of the cancer, traversing data representing a gene-to-gene or protein-to-protein interaction network specific for a type of the cancer type from a position of the received gene in the network to a position of at least one gene involved in metastasis for a tissue type, organ or body part, determining at least one shortest path in the network between the received gene and the at least one gene involved in metastasis for the tissue type, organ or body part, generating a prediction of metastasis to the tissue type based on the at least one determined path, and generating an output display indicating a likelihood of spread of cancer to the tissue type, organ or body part.

Described are genomic health risk metrics elaborated herein to hold significant advantages for the health care industry. The likelihood that any given GSV will be deleterious is relatively small. Since every human genome sequenced may result in several million GSVs, the advantage of a genomic health risk metric such as a tolerability score, an n-mer score, a context dependent tolerance score, or a protein tolerability score to clinicians is that it will allow them to focus on and prioritize deleterious mutations.

The disclosed inventions relate to web page templates and methods for constructing web pages, including web pages for the display of personal medical, genetic, and diagnostic information related to a physiological condition including, but not limited to, a medical condition, a trait, and a wellness condition.

A diagnostic analysis method and system is provided for identifying a microorganism from a genome sequence. Partially or fully assembled microbial genomes or short reads from whole-genome sequencing of microbial genomes are processed into a 4 MB Boolean array while preserving 1% of the genomic information in a way that allows for rapid comparison of a query genome to a large reference database. This represents a critical savings in storage space and speed by which large reference libraries can be queried.

Embodiments in the disclosure are directed to the use of distributed computing to align reads against multiple portions of a reference dataset. Aligned portions of the reference dataset that correspond with an above-threshold alignment score can be assessed for the presence of sparse indicators that can be categorized and used to influence a determination of a state transition likelihood. Various tasks associated with the processing of reads (e.g., alignment, sparse indicator detection, and/or determination of a state transition likelihood) may be able to take advantage of parallel processing and can be distributed among the machines while considering the resource utilization of those machines. Different load-balancing mechanisms can be employed in order to achieve even resource utilization across the machines, and in some cases may involve assessing various processing characteristics that reflect a predicted resource expenditure and/or time profile for each task to be processed by a machine.

A sample processing apparatus (102) includes a plurality of processing stations (108) configured to process a sample that includes a DNA sample and an ILS substance carried by a sample carrier. One of the plurality of processing stations includes an electrophoresis processing station. The sample processing apparatus further includes an optical reader (110) that generates a plurality of DNA sample color group signals and an ILS signal based on a result of the electrophoresis processing station. One of the DNA sample color group signals includes at least the locus amelogenin X-peak. The sample processing apparatus further includes an ILS signal validator (112) that validates peaks of the ILS signal as true peaks of the ILS signal only if the amelogenin X-peak of the one of the DNA sample color group signals is found between two peaks of the ILS signal.

A method and computer system for identifying genes associated with a phenotype includes obtaining data representing mutations in a cohort of subjects exhibiting a phenotype. An evolutionary action (EA) score is calculated for each mutation using the data obtained. For each gene in the cohort, respective distributions of the calculated EA scores are determined for mutations found in the gene. The determined distributions of EA scores are quantitatively compared within the cohort and with random distributions to establish comparison data. Based on the comparison data, distributions of EA scores are identified that are non-random, and linkage of each gene in the cohort to the phenotype is assessed based on the identified non-random distributions to identify genes associated with the phenotype. The phenotype can be a disease, such as cancer, and linkage of each gene in the cohort to the disease can be assessed to identify disease causing genes.

[Object] It is desirable to realize a more efficient coordination between the genetic analysis company and the medical institution. [Solution] Provided is an information processing device including: a data acquisition unit configured to acquire predetermined information; a determination unit configured to determine whether or not personal information of a subject has been set in the predetermined information; and an identification information provision unit configured to provide a medical institution terminal with user identification information required when the subject uses a service, in a case where it is determined that the personal information of the subject has not been set in the predetermined information.

The present application generally relates to the development of a vaccine, or the production of antibodies, capable of providing improved protection against a virus associated with ADF, such as Zika, Dengue and Ebola, based on novel antigenic peptides identified using an informational spectrum method (ISM).

Systems and methods are presented that allow for selection of tumor neoepitopes that are then used to generate a recombinant polytope that is optimized for proper trafficking and processing. In preferred methods, the polytope is encoded in a viral expression system that is used as a therapeutic agent.