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Related Experiment Videos

Sample Size Estimation for Detection of Splicing Events in Transcriptome Sequencing Data.

Wolfgang Kaisers1,2, Holger Schwender3,4, Heiner Schaal5,6

  • 1Department for Anaesthesiology, Heinrich Heine University, 40225 Düsseldorf, Germany. Wolfgang.Kaisers@uni-duesseldorf.de.

International Journal of Molecular Sciences
|September 6, 2017
PubMed
Summary

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Detecting rare splicing events in transcriptome data requires merging samples. A new probabilistic model helps determine the necessary number of replicates for observing these low-frequency events, improving analysis objectives.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Merging transcriptome data is crucial for detecting low-expressed transcripts and rare splicing events.
  • Determining the optimal number of biological replicates for such analyses is often challenging.

Purpose of the Study:

  • To develop and evaluate a probabilistic model for predicting the observation of rare events in transcriptome data.
  • To estimate the number of replicates needed for detecting low-frequency splicing events.

Main Methods:

  • A probabilistic model was developed to relate the number of observed events to observation probabilities.
  • The model was tested on 54 human dermal fibroblast transcriptome samples.
  • Analysis involved assessing putative splice-sites (alignment gap-sites) and their observation frequencies.
Keywords:
RNA-seqalternative splicingsplicingtranscriptome sequencingwgis

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Main Results:

  • The probabilistic model, with an added assumption, predicted observed event numbers within a 10% deviation.
  • A U-shaped pattern was observed in the probabilities of detecting splice-sites.
  • Single samples contained a substantial number of uniquely observed splicing events (e.g., 86,215 with STAR alignments).

Conclusions:

  • The probabilistic model adequately describes the observation of gap-sites in transcriptome data.
  • A simple binomial model can be used to calculate required sample sizes for sporadic events.
  • Including the observation of rare splicing events in analysis is advisable due to their abundance and biological relevance.