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Related Experiment Video

Updated: Dec 26, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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Error, noise and bias in de novo transcriptome assemblies.

Adam H Freedman1, Michele Clamp1, Timothy B Sackton1

  • 1Faculty of Arts and Sciences Informatics Group, Harvard University, Cambridge, MA, USA.

Molecular Ecology Resources
|March 18, 2020
PubMed
Summary

De novo transcriptome assembly, crucial for evolutionary studies, often violates assumptions of unbiased representation and accurate expression estimates. This study reveals significant nucleotide-level bias and underestimation of genetic diversity in assemblies.

Keywords:
adaptationbioinformatics/phyloinfomaticsgenomics/proteomicspopulation geneticstranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • De novo transcriptome assembly is widely used for evolutionary inferences.
  • It relies on assumptions of unbiased representation and accurate expression estimation.

Purpose of the Study:

  • To evaluate the validity of assumptions in de novo transcriptome assembly.
  • To identify and quantify biases in assembly and expression estimation.

Main Methods:

  • Analysis of publicly available transcriptome data from model organisms.
  • Assessment of nucleotide-level genotyping error rates.
  • Comparison of assembly-based expression estimates with map-to-reference methods.
  • Evaluation of standard filtering techniques.
  • Development and application of length-rescaled CPM for expression estimation.

Main Results:

  • Assumptions of de novo transcriptome assembly are consistently violated across algorithms and datasets.
  • Genotyping error rates range from 30% to 83%, leading to underestimation of genetic diversity and heterozygosity.
  • Expression estimates from assemblies deviate significantly from map-to-reference estimates, with positive bias at lower expression levels.
  • Standard filtering improves expression robustness but results in loss of highly expressed protein-coding genes.

Conclusions:

  • De novo transcriptome assemblies contain significant nucleotide and gene-level biases.
  • These biases impact evolutionary inferences and expression quantification.
  • Researchers must consider methods to mitigate bias in transcriptome assembly and analysis, such as length-rescaled CPM.