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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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RNA-seq03:21

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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. 
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Related Experiment Video

Updated: Mar 9, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis.

Maximilian Hastreiter1, Tim Jeske1, Jonathan Hoser1

  • 1Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.

Bioinformatics (Oxford, England)
|January 11, 2017
PubMed
Summary
This summary is machine-generated.

Automate Next Generation Sequencing (NGS) data analysis with modular workflows. This toolbox simplifies processing, enhances reproducibility, and ensures reliable handling of large datasets for DNAseq and RNAseq analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next Generation Sequencing (NGS) data analysis involves complex, large datasets and tool-chaining.
  • Standardization is needed to improve reliability, reproducibility, and maintainability of NGS data processing.

Purpose of the Study:

  • To present a modular workflow toolbox for automating NGS data analysis.
  • To enhance the reliability and ease of use in processing complex genomic datasets.

Main Methods:

  • Development of a documented, Linux-based toolbox with 42 processing modules.
  • Integration with the KNIME workflow management software.
  • Implementation of a High Throughput Executor (HTE) and a binary manager.

Main Results:

  • The toolbox facilitates various tasks, including DNAseq and RNAseq analysis.
  • The HTE improves reliability and reduces manual intervention for complex datasets.
  • The binary manager simplifies module acquisition and updates.

Conclusions:

  • The presented toolbox standardizes NGS tasks into modular workflows for automated analysis.
  • This approach significantly improves the reproducibility and maintainability of NGS data solutions.
  • The toolbox offers a robust solution for efficient and reliable genomic data processing.