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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Updated: Jun 20, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Targeted co-expression networks for the study of traits.

A Gómez-Pascual1, G Rocamora-Pérez2, L Ibanez3,4

  • 1Communications Engineering and Information Department, University of Murcia, 30100, Murcia, Spain.

Scientific Reports
|July 19, 2024
PubMed
Summary

Targeted Gene Co-expression Networks (TGCN) offer a novel approach to gene network analysis, generating smaller, more biologically relevant modules than traditional methods like WGCNA. This enhances the precision of identifying molecular pathways associated with specific traits, such as APP in Alzheimer's disease.

Keywords:
Co-expressionGenesLASSOTraitWGCNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Weighted Gene Co-expression Network Analysis (WGCNA) is a common method for gene co-expression network generation.
  • WGCNA often produces large, functionally complex modules that are difficult to interpret.
  • There is a need for more precise and biologically relevant gene network analysis tools.

Purpose of the Study:

  • To introduce Targeted Gene Co-expression Networks (TGCN) as a novel method for generating more focused gene co-expression modules.
  • To compare the performance of TGCN with WGCNA in terms of module precision and biological interpretability.
  • To demonstrate the utility of TGCN in identifying molecular pathways related to specific biological traits, using Alzheimer's disease as a case study.

Main Methods:

  • TGCN refines LASSO regression to identify key transcripts that predict a trait of interest based on gene expression.
  • Co-expression modules are constructed around these predictive transcripts.
  • Algorithm properties were validated using gene expression data from 13 brain regions (Genotype-Tissue Expression project).
  • An APP-TGCN was created using The Religious Orders Study and Memory and Aging Project dataset to investigate APP's role in Alzheimer's disease.

Main Results:

  • TGCN generates more precise gene co-expression modules compared to WGCNA.
  • These TGCN modules exhibit more specific yet biologically rich functional annotations.
  • The APP-TGCN successfully identified molecular pathways associated with APP in Alzheimer's disease.
  • Key findings were validated in two independent cohorts.

Conclusions:

  • TGCN provides a new framework for creating smaller, biologically relevant gene networks.
  • This method is advantageous for high-throughput, hypothesis-driven research.
  • TGCN enhances the ability to decipher complex biological relationships and identify disease-associated pathways.