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.

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.

Among other things, the present disclosure provides technologies for efficient and effective identification of ETaGs, for example, from fungi genomes. In some embodiments, provided technologies are particularly useful for identifying mammalian targets of biosynthetic products of fungi. In some embodiments, provided technologies are particularly useful for identifying and/or prioritizing human targets for drug development. In some embodiments, provided technologies are particularly useful for developing modulators for human targets based on biosynthetic products of fungi.

A construction method of ribosomal RNA database is provided, including the following steps: selecting a source of nucleic acid sequence database; performing normalization and homogenization on species classification rules; using AI technology for normalized classification and correction; selecting the kingdom to which the sequence species belongs; filtering out redundant sequences and sequences with inconsistent lengths; setting a threshold for unknown bases other than A, T, C or G, and excluding unknown bases that exceed the threshold; and excluding sequences with insufficient classification information.

Methods for non-invasive prenatal paternity testing are disclosed herein. The method uses genetic measurements made on plasma taken from a pregnant mother, along with genetic measurements of the alleged father, and genetic measurements of the mother, to determine whether or not the alleged father is the biological father of the fetus. This is accomplished by way of an informatics based method that can compare the genetic fingerprint of the fetal DNA found in maternal plasma to the genetic fingerprint of the alleged father.

Methods for non-invasive prenatal paternity testing are disclosed herein. The method uses genetic measurements made on plasma taken from a pregnant mother, along with genetic measurements of the alleged father, and genetic measurements of the mother, to determine whether or not the alleged father is the biological father of the fetus. This is accomplished by way of an informatics based method that can compare the genetic fingerprint of the fetal DNA found in maternal plasma to the genetic fingerprint of the alleged father.

Admixture generation determination includes: obtaining ancestry assignment information associated with an individual's genotype data, the ancestry assignment information at least indicating that a portion of the individual's genotype data is deemed to be associated with a specific ancestry; determining the individual's genetic ancestry summary data corresponding to the specific ancestry; estimating an admixture generation associated with the specific ancestry, the admixture generation indicating a most recent generation or a most recent generation range from which the individual has at least one non-admixed ancestor of the specific ancestry, the estimation including a maximum likelihood determination based at least in part on the individual's genetic ancestry summary data and a recombination model; and outputting the estimated admixture generation.

Admixture generation determination includes: obtaining ancestry assignment information associated with an individual's genotype data, the ancestry assignment information at least indicating that a portion of the individual's genotype data is deemed to be associated with a specific ancestry; determining the individual's genetic ancestry summary data corresponding to the specific ancestry; estimating an admixture generation associated with the specific ancestry, the admixture generation indicating a most recent generation or a most recent generation range from which the individual has at least one non-admixed ancestor of the specific ancestry, the estimation including a maximum likelihood determination based at least in part on the individual's genetic ancestry summary data and a recombination model; and outputting the estimated admixture generation.

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.