A computer-implemented method may obtain variant calling data for the tumor sample. The method may identify, in the variant calling data and in view of at least one population database, a list of germline variants for the tumor sample along each chromosome. The method may identify, in the variant calling data, a list of candidate somatic variants. The method may filter out likely germline variants from the list of candidate somatic variants to retain only likely somatic variants, filtering out the likely germline variants further comprising the steps of estimating a probability of each candidate somatic variant i being a germline variant (“Pgermline(i)”); and determine whether a candidate somatic variant i is germline or somatic, to retain only the likely somatic variants in the list of candidate somatic variants, determining the tumor mutational burden (TMB) value for the tumor sample.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Methods for performing procedures on single analytes at single-analyte resolution are disclosed. The methods utilize an iterative approach to performing a sequence of steps during a single-analyte process. Control of the single-analyte process is achieved by implementing actions during each iteration based upon one or more determined process metrics. Systems are also detailed for implementing the disclosed methods at single-analyte resolution.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.

Provided is a method of identifying a candidate gene for a genetic disease includes obtaining a disease network, disease-gene association information, and a gene network, obtaining a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs, and inter-relation data between genes and SNPs, creating a disease-gene-SNP multilayered network based on the disease network, the disease-gene association information, the gene network, the SNP network, and the interrelation data between genes and SNPs, and identifying a candidate gene for a genetic disease using the multilayered network.

Provided is a method of identifying a candidate gene for a genetic disease includes obtaining a disease network, disease-gene association information, and a gene network, obtaining a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs, and inter-relation data between genes and SNPs, creating a disease-gene-SNP multilayered network based on the disease network, the disease-gene association information, the gene network, the SNP network, and the interrelation data between genes and SNPs, and identifying a candidate gene for a genetic disease using the multilayered network.

A computer-implemented method for evaluating an amino acid chain is provided. The method includes obtaining first data including a representation of an amino acid chain and performing a process to generate second data comprising a set of one or more probability values. The representation comprises a sequence of two or more letters, each letter representing a respective amino acid. The second process comprises, for a said position in the sequence of letters, applying a language models to the sequence of letters to determine at least one probability value associated with the said position, wherein the language model is trained using one or more datasets representing amino acid chains. A computer system configured to implement the method, and a non-transitory computer-readable storage medium, storing instructions for implementing the method, is also provided.

A computer-implemented method for evaluating an amino acid chain is provided. The method includes obtaining first data including a representation of an amino acid chain and performing a process to generate second data comprising a set of one or more probability values. The representation comprises a sequence of two or more letters, each letter representing a respective amino acid. The second process comprises, for a said position in the sequence of letters, applying a language models to the sequence of letters to determine at least one probability value associated with the said position, wherein the language model is trained using one or more datasets representing amino acid chains. A computer system configured to implement the method, and a non-transitory computer-readable storage medium, storing instructions for implementing the method, is also provided.

Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.