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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Updated: Jun 25, 2026

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Global associations between copy number and transcript mRNA microarray data: an empirical study.

Wenjuan Gu1, Hyungwon Choi, Debashis Ghosh

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Cancer Informatics
|March 5, 2009
PubMed
Summary
This summary is machine-generated.

Combining gene expression and copy number data in cancer studies reveals weak global correlations but a strong cis-dosage effect. Segmenting copy number data improves these associations, enhancing genomic data integration.

Keywords:
circular binary segmentationhigh-dimensional datamachine learningtwo-color microarray platform

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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Cancer profiling studies increasingly measure both messenger RNA (mRNA) and copy number variations (CNVs).
  • Integrating these distinct genomic datasets presents a significant analytical challenge and opportunity.

Purpose of the Study:

  • To investigate the association between gene expression and copy number data across multiple cancer studies.
  • To determine the feasibility and optimal methods for combining these two types of genomic information.

Main Methods:

  • Analysis of transcript mRNA and copy number expression levels from various cancer profiling experiments.
  • Statistical assessment of correlations and dosage effects between gene expression and copy number data.
  • Evaluation of the impact of copy number segmentation on data integration.

Main Results:

  • A consistently weak but detectable global correlation was observed between gene expression and copy number across studies.
  • Strong evidence supports a cis-dosage effect, where copy number directly influences gene expression in nearby regions.
  • Segmenting copy number data significantly improved the correlation with gene expression levels.

Conclusions:

  • Despite weak global correlations, combining gene expression and copy number data is valuable, particularly by accounting for cis-dosage effects.
  • Copy number segmentation is a crucial preprocessing step for enhancing the integration of transcriptomic and genomic data in cancer research.
  • These findings facilitate more robust multi-omic analyses in cancer profiling.