Life Technologies Corporation

Sam Technology, Inc.

Harrison Leong

Nivedita Sumi Majumdar

David Hoard

Theodore E Straub

David Ruff

Kevin S Bodner

Alan S Gevins

Shiaw-Min Chen

Jeffrey A Marks

Ryan J Talbot

Elana Swartzman

Thomas Wessel

Manjula Aliminati

Levente Egry

Ruoyun Wu

Shakila Pothini

Michael E Smith

Edgar Schreiber

Weixiu Du

Isabel Sam-Vargas

Christopher Fong

Pratik Simon Soares

Salil Kumar

Theodore Straub

A proximity binding assay (PBA) is performed on at least one test sample, at least one reference sample, a background sample, and one or more calibration samples using a thermal cycler instrument. Ct values are determined for at least one set of test sample data and at least one set of reference sample data. Background corrected Ct values are calculated using a corresponding value in a background sample data set. A linear range is determined for the background corrected Ct values as a function of sample quantity. A linear regression line is calculated for each linear range. One or more parameter values of an exponential model (EM) fold change formula are estimated from the one or more sets of calibration sample data. A target protein quantity and associated confidence interval are calculated using the linear regression lines and the EM fold change formula.

Systems and methods for the analysis of proximity binding assay data

A method for generating a data visualization is provided. The method includes receiving a plurality of data points related to fluorescent emissions values from a plurality of reaction sites. The fluorescent emission values include information for a first type of dye and a second type of dye. The method further includes displaying a first portion of the plurality of data points related to the first type of dye in a representation of location of the plurality of reaction sites, and displaying a second portion of the plurality of data points related to the second type of dye in the representation. The method further includes displaying the first portion of the plurality of data points in a scatter plot display. The scatter plot shows fluorescent values related to the first dye on the y-axis and fluorescent values related to the second dye on the x-axis. The method includes displaying the second portion of the plurality of data points in the scatter plot display.

Methods and systems for visualizing data quality

Various embodiments of methods for analyzing proximity binding assay (PBA) data are disclosed. Proximity binding assays as a class of analyses offer the advantages of the sensitivity and specificity of biorecognition binding, along with the exponential signal amplification offered by a variety of oligonucleotide amplification reactions, such as the polymerase chain reaction (PCR). However, as various proximity binding assays have reaction kinetics governed by an additional step of the binding of a biorecognition probe (BRP) with a target molecule, there is a need for methods for the analysis of PBA data that are particularly suited to the unique characteristics of such data.

Methods for the analysis of proximity binding assay data

A method for generating a data visualization is provided. The method includes displaying a representation of a portion of detected data from a substrate to a user. The method further includes generating a data quality value for the portion of detected data and displaying, along with the representation of the portion of detected data, an indication of data quality value for the portion of detected data. The method further includes selecting, by the user, a quality value threshold, and displaying an adjusted indication of data quality value for the portion of detected data meeting the quality value threshold.

Visualization tools for digital PCR data

System and methods for the determination of a copy number of a target genomic sequence; either a target gene or genomic sequence of interest, in a biological sample are described. Various methods utilize a model drawn from a probability density function (PDF) for the assignment of a copy number of a target genomic sequence in a biological sample. Additionally, the methods provide for the determination of a confidence value for a copy number assigned to a sample based on attributes of the sample data. Additionally, various embodiments of an interactive graphical user interface (GUI) may provide an end-user with ready analysis of large sets of data representing a plurality of samples. In various embodiments of an interactive GUI, an end-user may be provided with a synchronized display of tabular and graphical sample data determined by an initial analysis according to a statistical model of a PDF. Such a synchronized display may enable an end-user to readily identify sample data for a subsequent analysis based on user input.

System for determining a copy number of a genomic sequence

A system for analyzing biological data, comprising: a storage configured to store a plurality of data files containing biological data obtained from a plurality of devices; a server configured to: host a plurality of applications, each configured to be implemented on the server and to provide analysis, manipulation, comparison, visualization, or a combination thereof, of the biological data included in the data files, wherein the plurality of applications allow a user to analyze different data files related to the same sample and compare the results of the analysis.

Methods and systems for knowledge discovery using biological data

A computer-implemented method for determining minor variants. The method includes receiving electropherogram sequence data from a test sample, identifying any non-primary peaks in the electropherogram, and characterizing identified non-primary peaks using at least one signal feature. The method may further include analyzing the at least one signal feature across identified non-primary peaks to identify variant candidates, evaluating at least one peak characteristic of each of the identified variant candidates, and classifying variant candidates as bona fide variants based on the evaluation of peak characteristics.

Methods and systems for detecting minor variants in a sample of genetic material

The present teachings relate to embodiments of systems and methods for the analysis of melt curve data for a plurality of samples. According to various embodiments, a melting temperature (T) may be determined across a range of different types of protein melt curve data, having variability over a plurality of analytical attributes in order to accommodate the complexity of protein melt curve data. The combination of a plurality of samples, coupled with the complexity of the data gives rise for a need to process the data in a manner that readily facilitates end-user to analysis of the data. Various embodiments of an interactive graphical user interface (GUI) according to the present teachings provide for rapid and sequential changes that may be made by an end user to displayed protein melt curve data to allow such analysis.

Systems and methods for the analysis of protein melt curve data

In one exemplary embodiment, a computer-implemented method for determining a genetic result from a biological sample is provided. The method includes receiving nucleic acid amplification data of a biological sample, by a processor, from a biological instrument. The method further includes storing translation data, in a memory. The translation data includes a pattern of assay values associated with possible genetic results. The method further includes comparing the translation data with the nucleic acid amplification data, by the processor, to generate the genetic result of the biological sample. Moreover, the method includes displaying the genetic result, on a display, to a user.

Methods and systems for determining meta-genotypes

A computer-aided training system uses electroencephalograms (EEGs) recorded from the trainee's scalp to alter the training protocol being presented by the computer, for example to present a new task to the trainee when he or she has mastered and automatized the current task. The index of the trainee's skill mastery and automatization is determined by analysis of the EEG using mathematical classification functions which distinguish different levels of skill acquisition. The functions are computed by computer neural networks and consist of a combination of EEG and other physiological variables which specifically characterize a trainee's level of focused attention and neurocognitive workload and his or her neurocognitive strategy. The functions are derived either for a group of trainees, or each trainee individually, performing a battery of one or more standard training tasks while wearing an EEG hat.

Neurocognitive adaptive computer-aided training method and system

In an EEG (electroencephalogram) system to detect brain waves from a subject, the adverse effect on the EEG recording of artifact due to head, body and eye movements is reduced. A head and body movement reference signal can be provided by an accelerometer, motion detector or, alternatively, spatial average of EEG channels. Eye motion sensors are used as a reference for eye movement artifacts. A composite reference signal consists of the head, body and eye movement reference signals. The contaminated EEG is the primary input to the adaptive movement and eye artifact canceler, in which an adaptive filter estimates the contaminants in the measured EEG data, and then subtracts them from the primary signal to obtain the corrected EEG data.

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San Francisco, CA, United States of America

Harrison Leong