Abstract: Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.

Abstract: Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.

Abstract: Disclosed herein are methods of detecting a target nucleic acid sequence, determining the localization of the target nucleic acid sequence, and/or quantifying the number of target nucleic acid sequences in a cell. This method may be used on small target nucleic acid sequences, and may be referred to as Nano-FISH.

Abstract: Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.

Abstract: Disclosed herein are methods of detecting a target nucleic acid sequence, determining the localization of the target nucleic acid sequence, and/or quantifying the number of target nucleic acid sequences in a cell. This method may be used on small target nucleic acid sequences, and may be referred to as Nano-FISH.

Abstract: Disclosed herein are methods of detecting a target viral nucleic acid sequence, determining the localization of the target viral nucleic acid sequence, and/or quantifying the number of target viral nucleic acid sequences in a cell. This method may be used on small target nucleic acid sequences, and may be referred to as Nano-FISH or viral Nano-FISH.

Abstract: The present disclosure provides methods and compositions for image based analysis and quantification of a protein load from protein (e.g., p53BP1) accumulation, induced by a cellular perturbation, such as administration of a genome editing tool comprising a DNA binding domain and a nuclease domain, a gene repressor, or a gene activator.

Abstract: Methods and apparatus to test and screen compounds in a multiplexed manner, using a mixture of genetically or functionally heterogeneous cells in common conditions.

Abstract: Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.

Abstract: This application provides a system and related methods that determine residue sequences for engineered proteins that facilitate genome engineering, including transcription activator-like effector nucleases. The system may receive an input DNA sequence for a region of a given genome and desired cleavage positions within the region. The system may determine candidate residue sequences for proteins that bind to the region and cleave the region at the desired cleavage positions, such as transcription activator-like effector nucleases (TALENs). The determination may be based on how the proteins may interact with the region and perform other biological functions. A selection can be made from the candidate residue sequences to achieve high accuracy and efficiency in the genome engineering tasks. The system may thus allow development of proteins that incorporate the selected residue sequences to perform the genome engineering tasks.

Abstract: Disclosed herein are methods of detecting a target nucleic acid sequence, determining the localization of the target nucleic acid sequence, and/or quantifying the number of target nucleic acid sequences in a cell. This method may be used on small V target nucleic acid sequences, and may be referred to as Nano-FISH.

Abstract: Disclosed herein are methods of detecting a regulatory element, determining the localization of a regulatory element, and/or measuring the activity of a regulatory element.

Abstract: Systems and methods for performing rapid genomic DNA analysis of samples, such as control samples and experimental samples. In one aspect, the system makes use of genomic DNA input, rather than gene expression input such as mRNA and/or cDNA associated with mRNA. The systems and methods perform statistical analyses on data generated from the samples to determine which DNA sequences in an identified set of DNA sequences have a basis of variation in an experimental sample when compared to a control sample, and additionally provide a quantitative measure of this variation. The quantitative measure may be based on metrics such as copy number and/or fold-change. The systems and methods employ this statistical framework in DNA-based evaluation settings, including the evaluation/diagnosis of a pathological condition such as cancer or transgenic analysis of transgenic plants and animals.

Abstract: Methods and applications of Global Patter Recognition (GPR), including a system for analyzing the results of real-time polymerase chain reaction (RT-PCR) experiments employing micro-titer and/or microarray plates and robotic plate readers is described. The system employs a set of self-normalizing housekeeping primers or oligonucleotides on the plates/arrays and an algorithmic approach to normalizing expression data from all primers on the plate based on the reaction products of several of the self-normalizing gene primers oligonucleotides. Normalization is accomplished using simplex reactions involving these self-normalizing primers/oligonucleotides; the normalization parameters are then useable across all control and experimental reactions of the plate/array. A ranked list of genes whose amount of change is statistically significant can be determined. The accuracy of this list is enhanced by the data normalization aspect of the system. Other applications of GPR are also disclosed herein.

Abstract: In certain embodiments, this present invention provides polypeptide compositions (e.g., antibodies and antigen binding portions thereof that bind to FcRn), and methods for modulating FcRn activity. In other embodiments, the present invention provides methods and compositions for treating autoimmune disorders.

Abstract: Methods and applications of Global Patter Recognition (GPR), including a system for analyzing the results of real-time polymerase chain reaction (RT-PCR) experiments employing micro-titer and/or microarray plates and robotic plate readers is described. The system employs a set of self-normalizing housekeeping primers or oligonucleotides on the plates/arrays and an algorithmic approach to normalizing expression data from all primers on the plate based on the reaction products of several of the self-normalizing gene primers oligonucleotides. Normalization is accomplished using simplex reactions involving these self-normalizing primers/oligonucleotides; the normalization parameters are then useable across all control and experimental reactions of the plate/array. A ranked list of genes whose amount of change is statistically significant can be determined. The accuracy of this list is enhanced by the data normalization aspect of the system. Other applications of GPR are also disclosed herein.

Abstract: Systems and methods for performing rapid genomic DNA analysis of samples, such as control samples and experimental samples. In one aspect, the system makes use of genomic DNA input, rather than gene expression input such as mRNA and/or cDNA associated with mRNA. The systems and methods perform statistical analyses on data generated from the samples to determine which DNA sequences in an identified set of DNA sequences have a basis of variation in an experimental sample when compared to a control sample, and additionally provide a quantitative measure of this variation. The quantitative measure may be based on metrics such as copy number and/or fold-change. The systems and methods employ this statistical framework in DNA-based evaluation settings, including the evaluation/diagnosis of a pathological condition such as cancer or transgenic analysis of transgenic plants and animals.