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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...
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...
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...

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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
07:18

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Published on: May 21, 2020

Transcript-specific expression profiles derived from sequence-based analysis of standard microarrays.

Anton G Moll1, Maja T Lindenmeyer, Matthias Kretzler

  • 1Institute of Physiology and Clinic for Nephrology, University of Zürich, Zürich, Switzerland.

Plos One
|March 12, 2009
PubMed
Summary
This summary is machine-generated.

This study reanalyzes microarray data to identify specific gene transcripts, revealing differential expression in human kidney tissue. Findings were confirmed using real-time RT-PCR, demonstrating transcript-specific analysis utility.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Alternative mRNA processing generates diverse splice isoforms with distinct functions.
  • Microarrays traditionally measure gene expression, but probe sets often lack transcript specificity.
  • Reanalyzing existing microarray data with transcript-specific probes can reveal expression levels of individual transcripts.

Purpose of the Study:

  • To develop and validate transcript-specific probe sets for analyzing Affymetrix microarray data.
  • To investigate differential gene expression at the transcript level in human kidney tissue.
  • To confirm microarray findings using independent experimental methods.

Main Methods:

  • Alignment of Affymetrix HG-U133A probe sequences with Ensembl transcript sequences to define transcript-specific probe sets.
  • Identification of 95,008 transcript-specific probes, grouped into 7,941 sets, differentiating 445 alternative transcripts.
  • Validation of predicted differential transcript expression in human kidney using real-time RT-PCR.

Main Results:

  • Successfully defined transcript-specific probe sets from existing microarray data.
  • Identified and differentiated 445 alternative transcripts from 215 genes.
  • Confirmed transcript-specific expression patterns for genes like PPM1A, PLG, and ABCA1 in human kidney tissue, including disease-specific changes.

Conclusions:

  • Transcript-specific analysis of microarray data enables the study of gene regulation at the individual transcript level.
  • This approach allows for the re-analysis of conventional microarray data to uncover transcript-specific expression patterns.
  • Predictions derived from transcript-specific probe set analysis are robust and can be validated by independent methods.