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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
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Related Experiment Video

Updated: Jan 28, 2026

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
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SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package.

Taewoon Joo1,2, Ji-Hye Choi1,2, Ji-Hye Lee1,2

  • 1Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.

BMC Bioinformatics
|February 22, 2019
PubMed
Summary

SEQprocess is a new R package framework for Next-Generation Sequencing (NGS) data processing. It offers customizable and reproducible pipelines for various applications, simplifying complex bioinformatics workflows for researchers.

Keywords:
Next generation sequencingPipelinePreprocessingRNA sequencingWhole exome sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-Generation Sequencing (NGS) is integral to biomedical research, but its data processing demands complex, customizable pipelines.
  • The rapid evolution of NGS applications and analysis methods necessitates adaptable processing tools.
  • Clinical applications like cell-free DNA and exosomal RNA sequencing require tailored bioinformatics pipelines.

Purpose of the Study:

  • To develop SEQprocess, a flexible framework for creating standard and customized Next-Generation Sequencing data processing pipelines.
  • To enhance the usability of NGS data analysis for biomedical scientists, including those without extensive bioinformatics expertise.

Main Methods:

  • SEQprocess is an R package featuring modular data processing steps for easy customization.
  • It includes six pre-customized pipelines, supporting tools like GATK for variant calling and TopHat2/Cufflink for RNA abundance.
  • Optimized pipelines for clinical sequencing data (cell-free DNA, miR-Seq) are also integrated.

Main Results:

  • The framework supports diverse NGS data types and processing needs, including clinical applications.
  • Processed data are converted to R-compatible formats ('ExpressionSet', 'SummarizedExperiment') for seamless downstream analysis.
  • Automated reports ensure the reproducibility of NGS data analysis workflows.

Conclusions:

  • SEQprocess offers a highly extendable, R-compatible framework for managing customized and reproducible Next-Generation Sequencing data processing.
  • It effectively integrates multiple legacy NGS processing tools, simplifying complex bioinformatics tasks.