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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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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.
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Real Time RT-PCR02:57

Real Time RT-PCR

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Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
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DNA Microarrays02:34

DNA Microarrays

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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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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
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A Whole-Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies: A Case Study

Kimmy A Stanton1, Patrick P Edger2, Joshua R Puzey3

  • 1Department of Biology, Whitman College, Walla Walla, Washington 99362.

G3 (Bethesda, Md.)
|March 5, 2017
PubMed
Summary
This summary is machine-generated.

Whole-transcriptome sequencing (RNA-seq) can identify reliable reference genes for quantitative PCR (qPCR). Environmental variation impacts gene expression variability more than means, influencing reference gene selection.

Keywords:
Mimulus guttatusMimulus luteusRNA-seqexpression stabilityquantitative RT-PCR

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Area of Science:

  • Plant genomics
  • Molecular biology
  • Gene expression analysis

Background:

  • Quantitative PCR (qPCR) relies on stable reference genes for accurate gene expression quantification.
  • Traditional reference genes may not be universally applicable across different species or conditions.
  • High-throughput transcriptomic data offers a powerful resource for identifying novel reference genes.

Purpose of the Study:

  • To identify and validate novel qPCR reference genes using RNA-sequencing (RNA-seq) in the plant genus *Mimulus*.
  • To evaluate the suitability of traditional housekeeping genes as reference genes.
  • To propose a generalizable method for reference gene discovery using transcriptomic data.

Main Methods:

  • RNA-sequencing (RNA-seq) was performed on *Mimulus* species.
  • Gene expression means and variability were analyzed and compared between RNA-seq and qPCR data.
  • The impact of environmental and genetic variation on gene expression was assessed.

Main Results:

  • RNA-seq accurately estimated gene expression means compared to qPCR.
  • Expression means were robust to moderate environmental and genetic variation.
  • Expression variability agreement between RNA-seq and qPCR was limited to samples from a shared environment, indicating environmental impact.

Conclusions:

  • Whole-transcriptome approaches, like RNA-seq, are valuable for identifying robust qPCR reference genes.
  • Environmental factors significantly influence gene expression variability, necessitating careful experimental design for reference gene validation.
  • Existing transcriptomic datasets provide a cost-effective resource for reference gene discovery in diverse organisms.