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Updated: Dec 28, 2025

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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iSeqQC: a tool for expression-based quality control in RNA sequencing.

Gaurav Kumar1, Adam Ertel2, George Feldman3

  • 1Cancer Genomics and Bioinformatics Laboratory, Sidney Kimmel Cancer Center, Department of Cancer Biology, BLSB 1009, Thomas Jefferson University, 233 South 10th Street, Philadelphia, PA-19107, USA. Gaurav.kumar@jefferson.edu.

BMC Bioinformatics
|February 15, 2020
PubMed
Summary
This summary is machine-generated.

iSeqQC is a new quality control tool for high-throughput sequencing data. It identifies outlier samples caused by experimental variability or biological differences, ensuring reliable research conclusions.

Keywords:
Count based QCExpression-based QCRNA seq QC toolRNA sequencing quality control

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quality control is essential for high-throughput sequencing to prevent compromised experiments.
  • Existing methods primarily address sequencing or alignment biases, with limited tools for outlier interpretation.

Purpose of the Study:

  • To develop an expression-based quality control tool for detecting outliers in high-throughput sequencing data.
  • To provide a solution for identifying biases arising from laboratory conditions or phenotypic dissimilarity.

Main Methods:

  • Implemented unsupervised clustering, agglomerative hierarchical clustering, and correlation coefficients.
  • Developed iSeqQC as an expression-based outlier detection tool.

Main Results:

  • iSeqQC successfully detects outliers in high-throughput sequencing data.
  • The tool identifies outliers stemming from variable laboratory conditions or phenotypic group dissimilarity.

Conclusions:

  • iSeqQC is a fast and lightweight tool for expression-based quality control.
  • The tool effectively detects outliers using various statistical approaches, enhancing data reliability.