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Related Concept Videos

Real Time RT-PCR02:57

Real Time RT-PCR

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Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
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Related Experiment Video

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments.

Wenfei Zhang1, Ying Liu2, Mindy Zhang3

  • 1Sanofi, Framingham, MA, USA. wenfei.zhang@sanofi.com.

BMC Systems Biology
|August 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian method to identify reproducible biological targets from high-throughput assays. The approach accurately distinguishes reliable signals from experimental variability, improving target selection.

Keywords:
Bayesian classificationEM algorithmEmpirical BayesGaussian mixtureHigh-throughput experimentReproducibility

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Biology
  • High-Throughput Screening

Background:

  • High-throughput assays are crucial for biological target identification but suffer from significant experimental variability.
  • Quantitative reproducibility analysis is essential for identifying reliable targets across replicate experiments.
  • Existing methods for reproducibility analysis have notable limitations.

Purpose of the Study:

  • To develop and present a novel statistical method for identifying reproducible targets from high-throughput screening data.
  • To address the limitations of current approaches in distinguishing true biological signals from noise.

Main Methods:

  • A Bayesian hierarchical model is employed to analyze test statistics from replicate experiments.
  • The model assumes that test statistics follow a mixture of multivariate Gaussian distributions.
  • Irreproducible targets are characterized by a zero-mean component within the distribution mixture.

Main Results:

  • Targets are classified as reproducible or irreproducible based on their posterior probability of belonging to the reproducible component.
  • The proposed method's performance was evaluated through simulations and a real biological data example.
  • The method demonstrated robust classification of reproducible targets.

Conclusions:

  • The proposed Bayesian method offers improved performance in identifying reproducible targets compared to existing techniques.
  • This approach enhances the reliability of target selection in high-throughput biological research.