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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Gene Duplication and Divergence02:37

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Updated: Aug 4, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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Leveraging gene correlations in single cell transcriptomic data.

Kai Silkwood1,2, Emmanuel Dollinger1,2,3, Josh Gervin1,2

  • 1Center for Complex Biological Systems, University of California, Irvine, Irvine CA.

Biorxiv : the Preprint Server for Biology
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

A new method, BigSur, analyzes single-cell RNA sequencing data without normalization to identify gene correlations. This approach reveals biological insights by uncovering gene communities and relationships in complex datasets.

Keywords:
gene co-expression networkgene regulatory networkgene-gene correlationmelanomasingle cell RNA sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Technical noise in single-cell RNA sequencing (scRNAseq) hinders the identification of rare cell types and gene regulatory networks.
  • Existing algorithms often require numerous parameters and lack controllable accuracy.
  • Establishing an appropriate null distribution for scRNAseq data is challenging due to unknown biological variation.

Approach:

  • Developed an analytical method assuming scRNAseq data comprises cell heterogeneity, transcriptional noise, and sampling error.
  • Analyzed scRNAseq data without normalization to avoid distribution skewing, especially in sparse data.
  • Introduced BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads) for feature selection and gene-gene correlation identification.

Key Points:

  • BigSur effectively captures weak yet significant correlation structures in scRNAseq data, validated with simulated datasets.
  • The method identifies thousands of gene correlations in human melanoma cell line data.
  • Clustering gene correlations reveals known cellular components and biological processes, suggesting novel relationships.

Conclusions:

  • Statistically grounded identification of gene-gene correlations offers new insights into functionally relevant gene regulatory networks.
  • BigSur provides a robust approach for analyzing scRNAseq data, improving feature selection and biological discovery.