Biofire Diagnostics, LLC

University of Utah Research Foundation

Biofire Defense, LLC

Thomas Charles Robbins

Charles Benjamin Cox

Randy P Rasmussen

Robert John Crisp

David J Eyre

Andrew Clinton Hemmert

Elizabeth Barker Campbell

David E Jones

Kirk M Ririe

Elizabeth Doman Copene

Benjamin William Galvin

Carl Thomas Wittwer

Robert Andrew Palais

Theodore Allan Wilson

Amber Cooper Kiser

Jay Jones

Kirk Max Ririe

Elizabeth Diana Barker Campbell

Techniques are provided for generating an array-specific range of Tm values to be used for calling a sample in a given array positive or negative for a target nucleic acid sequence. A sample well in an array is provided with a control sample containing a control nucleic acid sequence. The control sample is amplified by thermal cycling the sample well. A Tm value for the control sample is identified and compared to an expected Tm value for the control nucleic acid sequence to calculate a relationship between the identified control Tm value and the expected control Tm value. By applying this relationship to an expected Tm value for a target nucleic acid sequence, an array-specific range of Tm values for the target nucleic acid sequence is generated and can be used for calling an experimental sample in the same array positive or negative for the target nucleic acid sequence.

System and method for identifying analytes in assay using normalized Tm values

Methods and devices are provided for simultaneously amplifying a plurality of sample wells for a predetermined amount of amplification, detecting whether amplification has occurred in a first set of the wells, amplifying for an additional amount of amplification and detecting whether amplification has occurred in a second set of the wells. Methods are also provided for analyzing a target nucleic acid sequence using melt curves that were generated in a plurality of amplification cycles.

Multiple amplification cycle detection

Methods, sample vessels, and instruments are provided for melting curve analysis. A sample is amplified by thermal cycling the sample well between at least an annealing temperature and a denaturation temperature through n cycles, where each cycle includes an in-cycle denaturation step. Fluorescent data is collected during the in-cycle denaturation step of n cycles where n is at least two. Then a composite melting curve is generated using the fluorescent data collected during the denaturation step of each of the n cycles, and the sample is called using the composite melting curve, where the call is a positive or a negative call.

Detection using concurrent melting curves

Devices and methods are provided for spotting an array with fluid. Arrays produced by such methods are also provided. In one aspect of the invention, a spotter device for spotting a plurality of fluids into an array is described, the spotter device comprising a plurality of reservoirs provided in a first configuration, each reservoir holding its respective fluid, a print head having a plurality of positions provided in a second configuration, the second configuration being different from the first configuration, a plurality of tubes, each tube configured to provide fluid communication from a reservoir at a first end of the tube to a position in the print head at the second end of the tube, and a pump for pumping fluid through the tubes from the reservoir to the print head.

Microspotting device

An experimental melting curve is modeled as a sum of a true melting curve and background fluorescence. A deviation function may be generated based upon the experimental melting curve data and a model of a background signal. The deviation function may be generated by segmenting a range of the experimental curve into a plurality of windows. Within each window, a fit between the model of the background signal and the experimental melting curve data may be calculated. The deviation function may be formed from the resulting fit parameters. The deviation function may include background signal compensation and, as such, may be used in various melting curve analysis operations, such as data visualization, clustering, genotyping, scanning, negative sample removal, and the like. The deviation function may be used to seed an automated background correction process. A background-corrected melting curve may be further processed to remove an aggregation signal.

Systems and methods for automated melting curve analysis

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Salt Lake City, UT, United States of America

Thomas Charles Robbins