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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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DNA Microarrays02:34

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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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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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Analysis of Microarray and RNA-seq Expression Profiling Data.

Jui-Hung Hung, Zhiping Weng

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    This summary is machine-generated.

    Gene expression profiling measures many genes simultaneously using microarrays or RNA-sequencing. This guide covers essential data normalization and analysis techniques for accurate results.

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    Area of Science:

    • Genomics
    • Bioinformatics

    Background:

    • Gene expression profiling quantifies mRNA levels across numerous genes.
    • Experiments involve diverse cell types, treatments, and conditions.

    Purpose of the Study:

    • Introduce normalization and analysis for gene expression data.
    • Provide foundational knowledge for interpreting microarray and RNA-seq results.

    Main Methods:

    • Utilizes microarrays and next-generation sequencing (RNA-seq) for mRNA measurement.
    • Focuses on data processing techniques.

    Main Results:

    • Describes essential normalization strategies.
    • Outlines key data analysis approaches for expression profiling.

    Conclusions:

    • Accurate normalization and analysis are critical for reliable gene expression profiling.
    • This guide provides a basis for understanding experimental data.