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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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Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
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Published on: December 1, 2023

Computational analysis of RNA-seq.

Scott A Givan1, Christopher A Bottoms, William G Spollen

  • 1Department of Molecular Microbiology and Immunology, Informatics Research Core Facility, University of Missouri, Columbia, MO, USA. givans@missouri.edu

Methods in Molecular Biology (Clifton, N.J.)
|May 17, 2012
PubMed
Summary
This summary is machine-generated.

High-throughput DNA sequencing (HTS), or RNA-seq, offers deep gene expression analysis but requires specialized computational tools. This study provides a framework and scripts for managing, analyzing, and visualizing RNA-seq data efficiently.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology

Background:

  • High-Throughput DNA Sequencing (HTS) for gene expression analysis, known as RNA-sequencing (RNA-seq), is increasingly adopted.
  • RNA-seq offers superior data depth and breadth compared to traditional microarray techniques.
  • The complexity of RNA-seq data presents significant computational challenges.

Purpose of the Study:

  • To address the computational challenges associated with RNA-seq data analysis.
  • To provide a structured framework for managing, analyzing, and visualizing RNA-seq data.
  • To facilitate automation and data provenance tracking in RNA-seq workflows.

Main Methods:

  • Discussion of computational aspects including file management and data quality control.
  • Development of a standard nomenclature system for RNA-seq data.
  • Presentation of a general computational analysis workflow for RNA-seq.
  • Provision of a downloadable package of automation scripts.

Main Results:

  • A comprehensive framework for RNA-seq computational analysis is presented.
  • A standardized nomenclature system is proposed to enhance automation and data tracking.
  • A suite of scripts is available to automate the RNA-seq processing pipeline.

Conclusions:

  • Effective computational strategies are crucial for leveraging the full potential of RNA-seq.
  • The proposed framework and automation tools streamline RNA-seq data analysis.
  • Standardized procedures improve reproducibility and data management in genomic research.