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Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.

Zahra Zare Jousheghani1, Rob Patro2

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, 20742, Maryland, USA.

Biorxiv : the Preprint Server for Biology
|March 11, 2024
PubMed
Summary
This summary is machine-generated.

Oarfish is a new tool for long read transcript quantification. It improves accuracy by using a novel coverage score, outperforming existing methods for isoform analysis.

Keywords:
Oxford NanoporePacBioRNA-seqlong readstranscript quantification

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Long read sequencing is crucial for transcriptomics, enabling full-length isoform analysis.
  • Current methods primarily focus on transcript identification, neglecting quantification challenges.
  • Long read quantification faces hurdles due to technology, lower throughput, and technical artifacts.

Purpose of the Study:

  • Introduce oarfish, a novel method for long read transcript quantification.
  • Address the need for accurate quantification in long read transcriptomic data.
  • Enhance the analysis of full-length isoforms using long reads.

Main Methods:

  • Developed oarfish, a software tool for long read transcript quantification.
  • Incorporated a novel coverage score into the probabilistic model.
  • Evaluated oarfish against existing quantification methods.

Main Results:

  • Oarfish provides more accurate transcript quantification estimates.
  • The novel coverage score significantly improves estimation accuracy.
  • Oarfish excels in quantifying primary isoforms in cell lines and tissues.

Conclusions:

  • Oarfish offers a more precise approach to long read transcript quantification.
  • The method's innovative coverage score enhances isoform analysis.
  • Oarfish is available as open-source software for the research community.