Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

12.7K
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...
12.7K
Ribosome Profiling02:24

Ribosome Profiling

4.4K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
4.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation.

Journal of agricultural, biological, and environmental statistics·2026
Same author

Testing the effects of segmented crowdsource-selected messages to improve intentions to follow colorectal cancer screening recommendations: study protocol for a randomized controlled trial.

BMC public health·2026
Same author

Light cues induce protective anticipation of environmental water loss in terrestrial bacteria.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Adjusting for gene-specific covariates to improve RNA-seq analysis.

Bioinformatics (Oxford, England)·2023
Same author

High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.

Plant phenomics (Washington, D.C.)·2023
Same author

"Just right" combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation.

Immunity & ageing : I & A·2023

Related Experiment Video

Updated: Apr 16, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

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

Published on: November 7, 2025

553

SimSeq: a nonparametric approach to simulation of RNA-sequence datasets.

Sam Benidt1, Dan Nettleton1

  • 1Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA.

Bioinformatics (Oxford, England)
|March 1, 2015
PubMed
Summary

RNA sequencing analysis methods often rely on unrealistic models, leading to inflated performance estimates. A new nonparametric simulation algorithm provides a more accurate assessment of RNA-seq analysis method performance.

More Related Videos

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

11.1K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

43.1K

Related Experiment Videos

Last Updated: Apr 16, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

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

Published on: November 7, 2025

553
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

11.1K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

43.1K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genomics

Background:

  • RNA sequencing (RNA-seq) analysis methods frequently employ parametric models for read counts, which may not accurately reflect real-world data.
  • Performance evaluation of RNA-seq methods often uses data simulated from these assumed models, potentially yielding overly optimistic results.

Purpose of the Study:

  • To develop a data-based simulation algorithm for RNA-seq data that more realistically assesses analysis method performance.
  • To compare the performance of RNA-seq analysis methods when evaluated using parametric versus nonparametric simulation strategies.

Main Methods:

  • Developed a nonparametric simulation algorithm for RNA-seq data where simulated read counts match the distribution of user-provided source datasets.
  • Implemented the algorithm in the R package SimSeq, available on CRAN.
  • Compared statistical methods for RNA-seq analysis using simulations based on the negative binomial distribution and the proposed nonparametric algorithm.

Main Results:

  • RNA-seq analysis methods assuming parametric models performed better in false discovery rate control when data were simulated from parametric models.
  • Conversely, these methods showed less optimal false discovery rate control when evaluated using the more realistic nonparametric simulation strategy.
  • The nonparametric simulation strategy revealed a more accurate, and potentially less optimistic, view of method performance.

Conclusions:

  • Parametric modeling assumptions in RNA-seq analysis can lead to biased performance evaluations.
  • A nonparametric, data-based simulation approach offers a more realistic benchmark for assessing RNA-seq analysis tools.
  • The SimSeq R package provides a valuable resource for robust RNA-seq data simulation and method evaluation.