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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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Data-based RNA-seq simulations by binomial thinning.

David Gerard1

  • 1Department of Mathematics and Statistics, American University, Massachusetts Ave NW, Washington, DC, 20016, USA. dgerard@american.edu.

BMC Bioinformatics
|May 26, 2020
PubMed
Summary
This summary is machine-generated.

Simulating RNA-seq data from theoretical models can skew results. This study introduces realistic simulation techniques for RNA sequencing (RNA-seq) data, improving method assessment in non-ideal scenarios.

Keywords:
ConfoundersDifferential expressionFactor analysisRNA-seqScaling factorsSimulation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Numerous RNA sequencing (RNA-seq) analysis methods exist for bulk and single-cell data.
  • Current method assessment relies on simulated data, often from theoretical models.
  • Real-world RNA-seq data frequently violate theoretical model assumptions, potentially leading to inaccurate performance evaluations.

Purpose of the Study:

  • To develop and present novel simulation techniques for generating more realistic RNA sequencing datasets.
  • To enable robust assessment of RNA-seq analysis methods under non-ideal, model-violating conditions.
  • To provide tools for comparing methods on data that better reflects real-world complexities.

Main Methods:

  • Developed methods to introduce biologically relevant signals into existing real RNA-seq datasets.
  • Ensured simulated data retain realistic attributes, including deviations from theoretical models.
  • Applied simulation techniques to both single-cell and bulk RNA sequencing data.

Main Results:

  • The proposed simulation method generates more realistic RNA-seq datasets compared to traditional theoretical models.
  • The realistic simulation approach can alter conclusions drawn from differential expression analysis studies.
  • Demonstrated the utility of the approach by comparing factor analysis techniques on RNA-seq data.

Conclusions:

  • Simulation methods based on theoretical models can significantly impact study outcomes.
  • Introduced advanced, realistic simulation techniques for RNA sequencing data analysis.
  • The developed tools are available as the seqgendiff R package.