Validation Assays
Validation Assays - Related Videos
09:11
Assays for Validating Histone Acetyltransferase Inhibitors
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Cited by 6 •
2020
Inhibitors of histone acetyltransferases (HATs, also known as lysine acetyltransferases), such as CBP/p300, are potential therapeutics for treating cancer. However, rigorous methods for validating these inhibitors are needed. Three in vitro methods for validation include HAT assays with recombinant acetyltransferases, immunoblotting for histone acetylation in cell culture, and ChIP-qPCR.
01:29
Reliability and Validity
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2020
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen...
08:42
Application of a C. elegans Dopamine Neuron Degeneration Assay for the Validation of Potential Parkinson's Disease Genes
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Cited by 48 •
2008
This video demonstrates how to use C. elegans to assess dopaminergic neuron neurodegeneration as a model for Parkinson's disease. Furthermore, genetic screens are used to identify factors that either enhance degeneration or are neuroprotective.
11:00
Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
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Cited by 31 •
2018
This report describes a fast and reliable method for validating mRNA targets of cellular miRNAs. The method uses synthetic biotinylated Locked Nucleic Acid (LNA)-based miRNA mimics to capture target mRNA. Subsequently, streptavidin-coated magnetic beads are employed to pulldown the target mRNA for quantification by qPCR polymerase chain reaction.
01:03
Data Validation
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2023
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible. Nursing assessment guides are generally based on holistic models rather than medical...
FAQs
A validation plan examines several complementary performance characteristics rather than relying on a single result. Accuracy addresses agreement with an expected value, while precision concerns consistency of measurements. Sensitivity, specificity, and linearity describe other aspects of performance, and robustness tests how reliably the assay functions under relevant conditions. Together, these measures reveal whether the method is suitable for its intended target.
Controls, reference standards, and samples containing known quantities provide benchmarks for judging assay measurements. Comparing observed results with these benchmarks helps assess accuracy and identify whether the method detects the intended target reliably. This comparison also gives researchers a basis for evaluating performance characteristics rather than interpreting an isolated measurement without appropriate experimental context.
Assessing sensitivity and specificity together gives a broader view of assay performance than examining either characteristic alone. The pair helps researchers judge how effectively a method responds to its intended biological target while distinguishing relevant assay behavior. Including both in validation can expose limitations that might remain hidden if conclusions relied on only one performance measure.
Linearity and robustness contribute different information to assay evaluation. Considering both helps researchers examine whether performance characteristics remain appropriate across the intended testing conditions and recognize factors that may limit interpretation. Their inclusion strengthens decisions about whether a method is appropriate for biological research, quality control, or regulated laboratory workflows.
A validation workflow begins by specifying the intended target and selecting controls, reference standards, or samples with known quantities. Researchers then compare assay measurements with those benchmarks and examine performance characteristics, including accuracy, precision, sensitivity, specificity, linearity, and robustness. The resulting evidence is reviewed for limitations before the method is accepted for its intended use.
Validation assays are particularly relevant when a method must support clinical research, drug development, diagnostics, or regulated laboratory workflows. They also contribute to quality control during development of assays for nucleic acids, proteins, cells, or biological activity. In these settings, documented performance evidence helps determine whether results are sufficiently reliable for the method's intended purpose.
Results can show that an assay performs consistently, meets expected performance characteristics, or has limitations that affect interpretation. Researchers use these findings to confirm experimental conclusions, refine confidence in measurements, and decide whether a method is suitable for continued use. Validation therefore supports scientific decision-making and the reproducibility required in biological research.