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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...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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htSeqTools: high-throughput sequencing quality control, processing and visualization in R.

Evarist Planet1, Camille Stephan-Otto Attolini, Oscar Reina

  • 1Biostatistics and Bioinformatics Unit, Institute for Research in Biomedicine of Barcelona, Barcelona, Spain.

Bioinformatics (Oxford, England)
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bioconductor package for analyzing high-throughput sequencing data, specifically ChIP-seq and RNA-seq. The package offers tools for quality assessment, processing, and visualization, aiding in artifact detection and genomic region identification.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • High-throughput sequencing generates large datasets requiring specialized analysis tools.
  • ChIP-seq and RNA-seq are key applications in genomic research.

Purpose of the Study:

  • To present a comprehensive Bioconductor package for quality assessment, processing, and visualization of high-throughput sequencing data.
  • To offer tools for identifying and mitigating common artifacts in ChIP-seq and RNA-seq experiments.

Main Methods:

  • Development of a Bioconductor package.
  • Implementation of algorithms for outlier and bias detection.
  • Tools for identifying inefficient immunoprecipitation and overamplification artifacts.
  • Methods for de novo identification of read-rich genomic regions.
  • Visualization of genomic region location and coverage.

Main Results:

  • The package provides robust quality assessment for sequencing data.
  • It enables effective identification of common experimental artifacts.
  • The tools facilitate de novo discovery of regulatory elements and gene expression regions.
  • Comprehensive visualization aids in data interpretation.

Conclusions:

  • The Bioconductor package enhances the analysis of ChIP-seq and RNA-seq data.
  • It improves the reliability and interpretability of high-throughput sequencing results.
  • This toolset supports researchers in genomic studies by providing integrated analysis capabilities.