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Related Experiment Videos

Can transcriptome size be estimated from SAGE catalogs?

Michael D Stern1, Sergey V Anisimov, Kenneth R Boheler

  • 1Laboratory of Cardiovascular Science, Gerontology Research Center, National Institute on Aging, NIH, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA.

Bioinformatics (Oxford, England)
|March 4, 2003
PubMed
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Estimating transcriptome size using Serial Analysis of Gene Expression (SAGE) is challenging. Even large SAGE libraries may not accurately determine the number of unique transcripts due to sampling limitations.

Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Computational Biology

Background:

  • Serial Analysis of Gene Expression (SAGE) is a technique to quantify transcript abundance.
  • Estimating the total number of unique transcripts (transcriptome size) presents challenges.
  • Previous methods may be limited by sequencing and sampling errors.

Purpose of the Study:

  • To evaluate the accuracy of transcriptome size estimation using SAGE data.
  • To assess the impact of library size and sampling depth on estimation accuracy.
  • To identify constraints in designing SAGE experiments for determining biological complexity.

Main Methods:

  • Application of a simple estimator correcting for sequencing and sampling errors to SAGE data.
  • Generation of Monte Carlo simulated libraries with assumed 'true' expression level distributions.

Related Experiment Videos

  • Fitting SAGE data using a Monte Carlo model with a truncated inverse-square distribution.

    • Main Results:

    • The estimator required over 300,000 simulated tags to converge to the 'true' value (53,535) when using corrected data as ground truth.
    • SAGE data could be modeled by a distribution requiring 130,000 'true' transcripts and 10^6 samples for convergence.
    • Transcriptome size estimation from SAGE libraries is unreliable, especially with limited sampling.

    Conclusions:

    • Determining the precise size of a transcriptome from SAGE libraries is difficult, even with large datasets.
    • Accurate estimation necessitates sampling a number of tags inversely proportional to the unknown lowest abundance level.
    • This finding imposes limitations on SAGE experimental design for assessing biological complexity.