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Related Concept Videos

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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Related Experiment Video

Updated: Jun 24, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Computational and analytical framework for small RNA profiling by high-throughput sequencing.

Noah Fahlgren1, Christopher M Sullivan, Kristin D Kasschau

  • 1Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon 97331, USA.

RNA (New York, N.Y.)
|March 25, 2009
PubMed
Summary
This summary is machine-generated.

High-throughput sequencing (HTS) enables small RNA profiling but faces challenges. This study presents reproducible methods using synthetic RNA standards and statistical analysis for accurate small RNA quantification and data processing.

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AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Related Experiment Videos

Last Updated: Jun 24, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • High-throughput sequencing (HTS) offers direct small RNA profiling.
  • Existing HTS methods face limitations like representational artifacts and inadequate statistical analysis.
  • Processing and mapping massive HTS datasets pose computational challenges.

Purpose of the Study:

  • To establish a reproducible HTS method for quantitative small RNA profiling.
  • To introduce objective normalization and statistical analysis techniques for HTS data.
  • To develop computational tools for efficient small RNA data processing and mapping.

Main Methods:

  • Utilized cluster-based sequencing-by-synthesis technology for small RNA profiling in Arabidopsis thaliana.
  • Employed synthetic RNA oligoribonucleotide standards for inter-dataset normalization.
  • Adapted microarray-type statistical methods for analyzing multiple small RNA samples.
  • Developed computational algorithms for parsing, quantifying, and mapping small RNA data.

Main Results:

  • Demonstrated high reproducibility of cluster-based sequencing-by-synthesis for profiling various small RNAs.
  • Successfully validated the developed methods using Arabidopsis mutants deficient in small RNA biogenesis (dcl1, dcl2 dcl3 dcl4) and effector proteins (ago1).
  • Established robust computational pipelines for rapid and accurate small RNA data analysis.

Conclusions:

  • The presented HTS approach, combined with synthetic standards and statistical methods, provides a reliable platform for quantitative small RNA profiling.
  • The developed computational tools enhance the efficiency and accuracy of analyzing large-scale small RNA sequencing data.
  • This methodology facilitates deeper understanding of small RNA regulation in various biological contexts.