Abstract:
Systems and methods of providing run-time quality control and monitoring of a single or multiple sequencing runs are provided herein. In some embodiments, the run-time system includes or is in communication with a processor capable of determining various types of run-time information relating to the quality, progress, etc. of various sequencing runs. In some embodiments, the system can also be in communication with a user interface, for example, a GUI, capable of representing and communicating various types of information to a user regarding the quality of the individual or multiple runs, the functioning of the instrument, an error event, etc. Additionally, the system can capable of receiving actionable information from a user via the GUI thereby allowing the user to terminate or repeat various sequencing steps in a particular run, terminate a entire run, terminate all runs, allow a run to proceed, etc.
Abstract:
The present application provides for various embodiments of methods for the analysis of high resolution melt (HRM) curve data; where statistical assay variations in melt curve data may result from system noise in an analysis system. Such system noise may arise from various sources, such as the thermal non-uniformity of a thermocycler block in a thermal cycler apparatus, a detection system, etc. Additionally, various methods for the analysis of HRM curve data may provide an identification of a sample without the need for a user inputted information.
Abstract:
A method for improving color calls or base calls utilizes current and prior cycle multi-channel intensity data from a sequencing run to model residual cycle buildup. The model is applied to correct the multi-cycle channel intensity for the current cycle. The corrected multi-cycle channel intensity is used for color calls or base calls for the current cycle.
Abstract:
Systems and methods of providing run-time quality control and monitoring of a single or multiple sequencing runs are provided herein. In some embodiments, the run-time system includes or is in communication with a processor capable of determining various types of run-time information relating to the quality, progress, etc. of various sequencing runs. In some embodiments, the system can also be in communication with a user interface, for example, a GUI, capable of representing and communicating various types of information to a user regarding the quality of the individual or multiple runs, the functioning of the instrument, an error event, etc. Additionally, the system can capable of receiving actionable information from a user via the GUI thereby allowing the user to terminate or repeat various sequencing steps in a particular run, terminate a entire run, terminate all runs, allow a run to proceed, etc.
Abstract:
Disclosed are systems and methods for identifying microparticles or features arranged in high density arrays. Using the techniques of the present teachings allow for effective discrimination and characterization of microparticles such as sequencing beads or features having densities of about 39×106 particles/cm2 or more. In certain embodiments, such identification can be achieved via use of two or more images corresponding to respective subsets of the microparticles or features. In certain embodiments, microparticles in each such subset can be configured with a target that hybridizes with a labeled probe, thereby resulting in the corresponding image having lower density of objects to identify.
Abstract translation:公开了用于识别以高密度阵列排列的微粒或特征的系统和方法。 使用本教导的技术允许有效地区分和表征微粒,例如测量珠粒或具有约39×10 6个/ cm 2或更大密度的特征。 在某些实施例中,可以通过使用对应于微粒或特征的相应子集的两个或多个图像来实现这种识别。 在某些实施方案中,每个这样的子集中的微粒可以配置有与标记的探针杂交的靶,从而导致相应的图像具有较低密度的物体以识别。
Abstract:
The present teachings comprise systems and methods for calibrating the background or baseline signal in a PCR or other reaction. The background signal derived from detected emissions of sample wells can be subjected to a normalized statistical metric, and be compared to a threshold or other standard to discard outlier cycles or other extraneous data. According to various embodiments, a relative standard deviation (relativeSTD) for the background component can be generated by dividing the standard deviation by the median of differences across all wells, where the difference is defined as the difference between maximum and minimum pixel values of a well. The relativeSTD as a metric is not sensitive to machine-dependent variations in absolute signal output that can be caused by different gain settings, different LED draw currents, different optical paths, or other instrumental variations. More accurate background characterization can be achieved.