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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...
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Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: May 19, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

SIMPLEX: cloud-enabled pipeline for the comprehensive analysis of exome sequencing data.

Maria Fischer1, Rene Snajder, Stephan Pabinger

  • 1Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. maria.fischer@i-med.ac.at

Plos One
|August 8, 2012
PubMed
Summary

A new cloud-based pipeline automates exome sequencing analysis for rare genetic diseases. This tool simplifies complex data handling, enabling efficient candidate gene identification.

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Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Exome sequencing is crucial for identifying genes linked to rare genetic diseases.
  • Current data handling and analysis present significant challenges in exome studies.

Purpose of the Study:

  • To present a cloud-enabled autonomous analysis pipeline for comprehensive exome data processing.
  • To streamline the identification of candidate genes for rare genetic diseases.

Main Methods:

  • The pipeline integrates multiple applications for quality control, data filtering, alignment, variant detection (SNP and DIP), and functional annotation.
  • It offers customizable parameters and distributes computational tasks across high-performance computing or Amazon EC2 cloud environments.

Main Results:

  • The pipeline successfully automates the complete exome analysis workflow.
  • It has been utilized in research projects for rare genetic disease studies, demonstrating its practical application.

Conclusions:

  • This autonomous pipeline simplifies complex exome data analysis, accelerating rare genetic disease research.
  • The publicly available pipeline facilitates broader adoption and further development in genomic studies.