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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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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.
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Related Experiment Video

Updated: Aug 23, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Global FDR control across multiple RNAseq experiments.

Lathan Liou1, Milena Hornburg1, David S Robertson2

  • 1Merck Research Laboratories, Merck & Co., Kenilworth, NJ 07033, USA.

Bioinformatics (Oxford, England)
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

Controlling the global false discovery rate (FDR) across multiple RNA sequencing (RNAseq) experiments is crucial. Online FDR algorithms offer a principled and effective method for managing FDR in modern, large-scale RNAseq data analysis.

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Related Experiment Videos

Last Updated: Aug 23, 2025

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Classical methods for controlling the false discovery rate (FDR) in RNA sequencing (RNAseq) experiments are well-established.
  • However, analyzing RNAseq experiments individually can inflate the overall FDR due to modern research workflows and large databases.
  • A new paradigm is needed to control FDR globally across numerous RNAseq studies.

Purpose of the Study:

  • To propose and evaluate a novel methodology for controlling the global FDR across multiple RNA sequencing experiments.
  • To address the inflation of overall FDR caused by separate analyses of individual RNAseq studies.
  • To implement and demonstrate the effectiveness of online multiple hypothesis testing for global FDR control.

Main Methods:

  • Application of recently developed online multiple hypothesis testing methodology.
  • Comparison of online FDR control algorithms with repeated application of classical offline approaches.
  • Simulation studies to assess the performance and power of online versus offline FDR control methods.

Main Results:

  • Repeated application of classical offline FDR control methods shows variable control of global FDR over time in RNAseq experiments.
  • Online FDR algorithms provide a principled approach to controlling the global FDR across multiple RNAseq experiments.
  • In simulation scenarios, online FDR approaches demonstrate comparable statistical power to repeated offline methods.

Conclusions:

  • Online FDR algorithms represent a robust and principled strategy for managing the global FDR in large-scale RNAseq data analysis.
  • The proposed online methods offer a solution to the FDR inflation problem encountered with traditional, experiment-wise analysis.
  • The onlineFDR package is available for implementing these advanced FDR control techniques in bioinformatics research.