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Improved data-driven likelihood factorizations for transcript abundance estimation.

Mohsen Zakeri1, Avi Srivastava1, Fatemeh Almodaresi1

  • 1Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.

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|September 9, 2017
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Summary
This summary is machine-generated.

Approximate likelihood factorizations in transcript abundance estimation can reduce accuracy for related transcripts. Improved data-driven factorizations in Salmon achieve high accuracy while maintaining computational efficiency.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Transcript abundance estimation methods often use approximate likelihood function factorizations to speed up computation.
  • These simplifications, while efficient, can discard information crucial for accurate transcript quantification.
  • Previous approaches may struggle with estimating abundances of highly related transcripts due to these simplifications.

Purpose of the Study:

  • To investigate the impact of likelihood function factorizations on transcript abundance estimation accuracy.
  • To develop improved, data-driven factorization methods that retain computational efficiency.
  • To enhance the accuracy of transcript quantification, especially for closely related transcripts.

Main Methods:

  • Implemented data-driven factorizations within the Salmon transcript quantification tool.
  • Evaluated the accuracy of simplified versus unsimplified likelihood models.
  • Focused on transcript-fragment compatibility and conditional fragment probabilities.

Main Results:

  • Model simplifications based solely on transcript-fragment compatibility can decrease accuracy for related transcripts.
  • Data-driven factorizations, incorporating conditional probabilities, significantly improve accuracy.
  • The proposed methods achieve accuracy comparable to complete likelihood models while maintaining computational speed.

Conclusions:

  • Approximate likelihood factorizations in transcript abundance estimation can be optimized for accuracy.
  • Data-driven approaches offer a balance between computational efficiency and precise quantification.
  • The enhanced Salmon tool provides a robust solution for accurate transcript abundance estimation.