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 Experiment Video

Updated: Jun 24, 2026

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

Statistical analysis of expressed sequence tags.

Edward Susko1, Andrew J Roger

  • 1Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada.

Methods in Molecular Biology (Clifton, N.J.)
|March 12, 2009
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...

You might also read

Related Articles

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

Sort by
Same author

GTRspmix: Capturing Heterogeneity of Exchangeabilities Across Sites to Improve Protein Phylogenetics.

bioRxiv : the preprint server for biology·2026
Same author

Modeling Site-and-Branch-Heterogeneity with GFmix.

Systematic biology·2026
Same author

IQ-TREE 3: phylogenomic inference software using complex evolutionary models.

Molecular biology and evolution·2026
Same author

Comparing partition and mixture models with akaike information criteria.

Systematic biology·2026
Same author

The LECA had a conventional kinetochore and the kinetoplastid kinetochore is a derived feature - a critical evaluation of Akiyoshi, 2025.

Journal of cell science·2026
Same author

Pangenome biology and evolution in harmful algal-bloom-forming pelagophytes.

Current biology : CB·2025
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Comparing expressed sequence tag (EST) libraries helps identify which ones are most likely to yield new gene discoveries. Statistical methods assess gene representation and expression levels between libraries for efficient gene characterization.

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Expressed sequence tag (EST) surveys are crucial for characterizing organismal genes.
  • Gene discovery rates in EST surveys are influenced by cDNA library redundancy.

Purpose of the Study:

  • To develop statistical methods for comparing EST libraries.
  • To determine which libraries are most likely to yield new gene discoveries.

Main Methods:

  • Analyzing gene frequencies within subsamples of EST reads.
  • Implementing statistical tests for gene representation and expression equality between libraries.
  • Applying multiple correction adjustments for statistical validity.

Main Results:

  • Developed statistics to compare EST libraries based on gene occurrence frequencies.

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Related Experiment Videos

Last Updated: Jun 24, 2026

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

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

  • Presented methods to test for equal gene representation or expression in paired libraries.

    • Conclusions:

    • Statistical comparisons of EST libraries enhance the efficiency of gene discovery.
    • These methods aid in selecting optimal libraries for future sequencing efforts.