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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Combinatorial Gene Control02:33

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Related Experiment Video

Updated: Jun 15, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Transcriptome data are insufficient to control false discoveries in regulatory network inference.

Eric Kernfeld1, Rebecca Keener1, Patrick Cahan2

  • 1Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA.

Cell Systems
|August 22, 2024
PubMed
Summary

Inferring gene regulatory networks from gene expression data is prone to errors. A new method, model-X knockoffs, improves accuracy by controlling false discoveries and accounting for complex biological factors like indirect regulation and nonlinear effects.

Keywords:
Markov random fieldfalse discovery rategene regulatory networkknockoff filternetwork inferencestructure learningtranscription factortranscriptional regulation

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Transcriptional regulatory network (TRN) inference from transcriptomic data often yields high false positive rates.
  • Existing methods struggle with distinguishing direct vs. indirect regulation, nonlinear effects, and unmeasured confounding variables.

Purpose of the Study:

  • To introduce and evaluate the model-X knockoffs framework for FDR control in TRN inference.
  • To address limitations of previous methods by accounting for indirect effects, nonlinearities, and covariates.

Main Methods:

  • Application of the model-X knockoffs statistical framework to transcriptomic data.
  • Adjustment of the procedure for estimating FDR against incomplete gold standards.
  • Benchmarking against ChIP-seq and other gold standards.

Main Results:

  • The model-X knockoffs framework effectively controls FDR while accommodating indirect effects and nonlinear dose-response.
  • Observed FDR exceeded reported FDR when benchmarked against gold standards, highlighting the impact of unmeasured confounding.
  • The method demonstrates improved accuracy in inferring causal TRNs.

Conclusions:

  • Model-X knockoffs offers a robust approach to enhance the reliability of causal TRN inference.
  • Unmeasured confounding remains a significant challenge in TRN inference, necessitating further methodological development.
  • This study provides a more accurate framework for understanding gene regulatory mechanisms.