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Related Concept Videos

RNA-seq03:21

RNA-seq

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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Quality Control01:05

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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The cell cycle regulation directs how a cell proceeds from one phase to the next and begins mitosis. The cell cycle control system includes intracellular regulatory molecules and external triggers. They provide "stop" or "advance" signals and operate at specific cell cycle stages termed checkpoints to ensure that a particular process is completed before the cell advances to the next phase.
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Related Experiment Video

Updated: Jan 29, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

367

Quality Control of Single-Cell RNA-seq.

Peng Jiang1

  • 1Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI, USA. PJiang@morgridge.org.

Methods in Molecular Biology (Clifton, N.J.)
|February 14, 2019
PubMed
Summary

Single-cell RNA sequencing (scRNA-seq) helps study cell variability. This protocol distinguishes technical noise from true biological signals in scRNA-seq data, improving analysis accuracy.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing cellular heterogeneity.
  • Distinguishing technical noise from true biological variation in scRNA-seq data is a significant challenge.
  • Accurate identification and removal of technical artifacts are crucial for reliable downstream analyses.

Purpose of the Study:

  • To develop and present a robust protocol for detecting technical artifacts in scRNA-seq samples.
  • To improve the reliability of scRNA-seq data by effectively separating technical noise from biological variation.
  • To provide a method that integrates gene expression patterns with data quality metrics.

Main Methods:

  • The protocol integrates analysis of gene expression patterns.
Keywords:
Data qualityGene expression patternsIntegrateQuality controlscRNA-seq

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  • It incorporates data quality metrics for artifact detection.
  • The method aims to differentiate technical noise from biological variability.

    • Main Results:

    • The presented protocol effectively detects technical artifacts in scRNA-seq data.
    • It allows for better separation of technical noise from genuine biological variation.
    • The approach enhances the quality of scRNA-seq datasets for further investigation.

    Conclusions:

    • The developed protocol offers a reliable method for identifying technical artifacts in scRNA-seq.
    • Implementing this protocol can significantly improve the accuracy and interpretability of scRNA-seq studies.
    • This work addresses a critical challenge in single-cell data analysis, facilitating more robust biological discoveries.