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

Ribosome Profiling02:24

Ribosome Profiling

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Mechanisms involved in ceramide-induced cell cycle arrest in human hepatocarcinoma cells.

World journal of gastroenterology·2007
Same author

A population-based survey of women's traditional postpartum behaviours in Northern China.

Midwifery·2007
Same author

A glimpse of streptococcal toxic shock syndrome from comparative genomics of S. suis 2 Chinese isolates.

PloS one·2007
Same author

Colon carcinoma cells harboring PIK3CA mutations display resistance to growth factor deprivation induced apoptosis.

Molecular cancer therapeutics·2007
Same author

[Surgical treatment of 402 consecutive cases for hilar cholangiocarcinoma: Chinese single center experience].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2007
Same author

Highly convergent route to cyclopeptide alkaloids: total synthesis of ziziphine N.

Organic letters·2007

Related Experiment Video

Updated: Mar 21, 2026

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs
10:28

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs

Published on: April 14, 2015

34.0K

Selecting Reliable mRNA Expression Measurements Across Platforms Improves Downstream Analysis.

Pan Tong1, Lixia Diao1, Li Shen1

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Cancer Informatics
|May 21, 2016
PubMed
Summary

This study introduces a statistical method to identify reliable gene expression measurements across different platforms like microarrays and RNA sequencing. The approach improves data quality for gene signature development and functional analysis.

Keywords:
RNA sequencebeta-mixture modelcorrelation coefficientscross-validationgene expressionprobe selection

More Related Videos

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.8K
Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice
07:07

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

6.6K

Related Experiment Videos

Last Updated: Mar 21, 2026

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs
10:28

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs

Published on: April 14, 2015

34.0K
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.8K
Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice
07:07

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

6.6K

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Modeling

Background:

  • Publicly available gene expression datasets are increasingly used for research.
  • Data quality is critical for downstream analyses, including gene signature development and cross-validation.
  • Reliable identification of expression measurements across multiple platforms is an important analytical challenge.

Purpose of the Study:

  • To propose a statistical framework for selecting reliable mRNA expression measurements between platforms.
  • To provide an effective and objective method for separating high-quality probes from low-quality ones.
  • To improve the efficiency and accuracy of gene expression data analysis.

Main Methods:

  • Developed a statistical framework using a beta-mixture model to analyze correlations in mRNA expression levels.
  • Applied the model to compare gene expression measurements from Affymetrix and Illumina microarray platforms.
  • Validated the approach by comparing Affymetrix microarray data with RNA sequencing (RNA-Seq) measurements.

Main Results:

  • The proposed algorithm successfully identified probes and genes with reliable measurements across different platforms.
  • Removing unreliable measurements led to significant improvements in gene signature development.
  • Enhanced accuracy was observed in functional annotations after data quality improvement.

Conclusions:

  • The beta-mixture model provides an effective and objective approach for assessing the reliability of gene expression measurements.
  • The method is applicable for comparing various expression platforms, including microarrays and RNA-Seq.
  • Improving data quality through reliable measurement selection enhances the utility of gene expression datasets for biological discovery.