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Related Experiment Video

Updated: Apr 12, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq.

Joshua Xu1, Zhenqiang Su1, Huixiao Hong1

  • 1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration , Jefferson, Arkansas 72079, USA.

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Summary

The Sequencing Quality Control (SEQC) project evaluated RNA-sequencing (RNA-Seq) technology using extensive data and built-in controls. This research provides a foundation for improving RNA-Seq applications in genomic studies.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Whole-transcriptome sequencing (RNA-Seq) is revolutionizing genomic research.
  • Understanding RNA-Seq's capabilities and limitations is crucial for reliable genomic data.
  • The US Food and Drug Administration (FDA) initiated the MicroArray Quality Control (MAQC)-III project, or SEQC project, to address this need.

Purpose of the Study:

  • To rigorously assess the performance of multiple RNA-sequencing platforms.
  • To establish standardized quality control metrics for RNA-Seq experiments.
  • To generate a large-scale, high-quality transcriptomic dataset with known truths for benchmarking.

Main Methods:

  • Utilized two well-characterized human reference RNA samples.
  • Employed three different sequencing platforms across multiple independent sites.
  • Incorporated external RNA controls (ERCC), titration data, and quantitative PCR (qPCR) for verification.

Main Results:

  • Generated over 30 billion sequence reads, creating an unprecedented ultradeep transcriptomic dataset.
  • Provided a comprehensive evaluation of RNA-Seq platform performance and data quality.
  • Established a valuable resource with known truths for method development and validation.

Conclusions:

  • The SEQC project successfully generated a massive RNA-Seq dataset with built-in controls.
  • This dataset serves as a critical benchmark for advancing RNA-Seq technology and its applications.
  • Findings will drive improvements in the accuracy and reliability of transcriptomic analyses.