Provided herein is a method for analyzing polynucleotides such as genomic DNA. In certain embodiments, the method comprises: (a) treating chromatin isolated from a population of cells with an insertional enzyme complex to produce tagged fragments of genomic DNA; (b) sequencing a portion of the tagged fragments to produce a plurality of sequence reads; and (c) making an epigenetic map of a region of the genome of the cells by mapping information obtained from the sequence reads to the region. A kit for performing the method is also provided.

Provided herein is a method for analyzing polynucleotides such as genomic DNA. In certain embodiments, the method comprises: (a) treating chromatin isolated from a population of cells with an insertional enzyme complex to produce tagged fragments of genomic DNA; (b) sequencing a portion of the tagged fragments to produce a plurality of sequence reads; and (c) making an epigenetic map of a region of the genome of the cells by mapping information obtained from the sequence reads to the region. A kit for performing the method is also provided.

Methods and systems for determining copy number variants are disclosed. An example method can comprise applying a sample grouping technique to select reference coverage data, normalizing sample coverage data comprising a plurality of genomic regions, and fitting a mixture model to the normalized sample coverage data based on the selected reference coverage data. An example method can comprise identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model. An example method can comprise outputting the one or more copy number variants.

Methods and systems for determining copy number variants are disclosed. An example method can comprise applying a sample grouping technique to select reference coverage data, normalizing sample coverage data comprising a plurality of genomic regions, and fitting a mixture model to the normalized sample coverage data based on the selected reference coverage data. An example method can comprise identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model. An example method can comprise outputting the one or more copy number variants.

Provided herein are methods of identifying a protein capable of binding a ligand, the method comprising: (a) contacting the ligand with two or more samples comprising a plurality of proteins in a solution; (b) separating the proteins bound to the ligand (“bound proteins”) from the proteins that are not bound to the ligand (“unbound proteins”) in each sample; (c) denaturing and digesting the bound proteins to form a plurality of peptides in each sample; (d) quantifying a plurality of molecular features contained in the plurality of peptides in each sample, wherein the molecular features are defined as having a mass to charge ratio, retention time, and peak intensity as measured by mass spectrometry; and (e) ranking the molecular features that exhibit a statistically significant difference in quantity between the samples contacted with the ligand and a sample that is not contacted with the ligand (“statistically significant molecular feature”).

Provided herein are methods of identifying a protein capable of binding a ligand, the method comprising: (a) contacting the ligand with two or more samples comprising a plurality of proteins in a solution; (b) separating the proteins bound to the ligand (“bound proteins”) from the proteins that are not bound to the ligand (“unbound proteins”) in each sample; (c) denaturing and digesting the bound proteins to form a plurality of peptides in each sample; (d) quantifying a plurality of molecular features contained in the plurality of peptides in each sample, wherein the molecular features are defined as having a mass to charge ratio, retention time, and peak intensity as measured by mass spectrometry; and (e) ranking the molecular features that exhibit a statistically significant difference in quantity between the samples contacted with the ligand and a sample that is not contacted with the ligand (“statistically significant molecular feature”).

Cumulus cell (CC) gene expression is being explored as an additional method to morphological scoring to choose the embryo with the highest chance to pregnancy. The present invention relates to a novel method of identifying biomarker genes for evaluating the competence of a mammalian oocyte in giving rise to a viable pregnancy after fertilization, based on the use of live birth and embryonic development as endpoint criteria for the oocytes to be used in an exon level analysis of potential biomarker genes. The invention further provides CC-expressed biomarker genes thus identified, as well as prognostic models based on the biomarker genes identified using the methods of the present invention.

Cumulus cell (CC) gene expression is being explored as an additional method to morphological scoring to choose the embryo with the highest chance to pregnancy. The present invention relates to a novel method of identifying biomarker genes for evaluating the competence of a mammalian oocyte in giving rise to a viable pregnancy after fertilization, based on the use of live birth and embryonic development as endpoint criteria for the oocytes to be used in an exon level analysis of potential biomarker genes. The invention further provides CC-expressed biomarker genes thus identified, as well as prognostic models based on the biomarker genes identified using the methods of the present invention.

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.