The present technology encompasses systems for predicting a characteristic of an individual subject, as well as methods for predicting a characteristic of a particular subject based on the systems of the technology disclosed herein. The disclosure herein also provides methods to improve a characteristics in a subject, based on the prediction produced by the system.

System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-window cM widths are used to estimate a degree of ancestral relatedness between two individuals.

A computer-implemented method of preparing a set of differentially informative methylated positions (DIMPs) or differentially informative methylated regions (DIMRs) from a sample methylome of an animal or plant having a phenotypic characteristic different from a wild-type of the same species of animal or plant, and the characteristic is associate with differences in methylation of the genome, comprises: providing a computer with the sample methylome, and a reference methylome of the wild-type of the same species of animal or plant; calculating with the computer a divergence between a plurality of cytosine positions of the sample methylome and the reference methylome; and selecting with the computer a set of DIMPs or DIMRs. Each DIMP or DIMR is selected based on an approximation of the energy required to produce the divergence between methylation levels of the plurality of cytosine positions of the sample methylome as compared to the wild-type methylome.

Generation of biomolecule sequence coevolution data structures, matrices, scores, and sectors are described. Generally, the generated coevolution data removes covariant noise due to phylogenetic drift and can reveal coevolution of residue positions in multiple phylogenetic distances. Scores can be built upon the data structures and matrices to reveal sectors of residue positions that function and evolve together. Furthermore, the coevolution data structures, matrices, scores, and sectors can be used to predict structure or function of residue variants.

Methods for predicting a disease free time interval (DFI) for a cancer patient under consideration for initial or further chemotherapy treatment are disclosed. The methods include obtaining a biological sample from a patient and detecting a copy number of chromosome region A1 and/or C2. The mean copy number per cell is correlated with a DFI for the subject. The chemotherapy can include doxorubicin and/or L-asparaginase treatment. Also provided are kits for predicting DFI in a subject with cancer and computer readable storage media for performing the presently disclosed methods.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in the exploration, determination, production and recovery of natural resources, including energy sources, and the monitoring, control and analysis of processes and activities.

Methods, systems and kits to detect and/or quantify lupus and disease progression in a subject having, or at risk of having, lupus are disclosed.

A representation of a nucleic acid sequence encodes a particular gene having at least one intron. An intron signature value corresponding to the at least one intron is determined based on a first computational function applied to at least one portion of the representation of the nucleic acid sequence corresponding to the at least one intron. A protein signature value is determined, being based on a second computational function applied to a representation of a protein. In a database, an association is formed between the intron and protein signature values. This process is repeated for each of a plurality of nucleic acid sequences. Nucleic acid sequences in the database are ordered based on a sort of corresponding intron signature values. An ordering determined by the sort is used to determine or confirm a role or function of a portion of a given nucleic acid sequence.

There are no reliable clinical bio-markers of survival prognosis, patient's risk stratification and treatment prediction for epithelial ovarian cancers(EOC). The most common type of the human EOC is a high grade serous EOC. This cancer is characterized with one of the lowest survival rates compared to other cancers. The present invention relates to an method for a prognosis of survival of a subject diagnosed with EOC, the method comprising determining in a sample of the subject gene expression level of at least one gene in the list of Evi1 pathway genes; and/or copy number of at least one gene in the MECOM locus; wherein the level against at least one expression threshold value will define the risk group of the subject and/or a risk of the disease progression after surgery treatment, and/or an effectiveness of post-surgery chemotherapy. The quantification method of Evi1/MECOM locus regulatory pathway provides a set of multigene prognostic signatures representing EVI1 pathway modules, which collectively provided a framwork of high-confidence, sensitive and specific prognosis assay(s) of EOC and stratification method for the EOC patient stratification according to disease relapse.

Systems and methods for isolating DNA molecules that can include the steps of: providing a transposon mutant collection, the transposon mutant collection being stored in a plurality of wells; dispatching aliquots from each well in the transposon mutant collection to a set of pools in a combinatorial pooling array that uses pool address coordinates, the aliquots being dispatched to the set of pools based on a location of the aliquots within the transposon mutant collection; constructing an amplicon library, the amplicon library including a sequencing dataset; parsing the sequencing dataset to a set of putative transposon insertion locations and pool address coordinates; calculating a likelihood for each location and pool address coordinates within the set of putative transposon insertion locations and pool address coordinates using a Bayesian inference algorithm informed by internal self-consistency; and generating a progenitor collection catalog based on location and pool address coordinates verified in the calculating step.