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Updated: Sep 7, 2025

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Shepherd: accurate clustering for correcting DNA barcode errors.

Nik Tavakolian¹, João Guilherme Frazão², Devin Bendixsen²

¹Department of Mathematics, Stockholm University, Stockholm 10691, Sweden.

Bioinformatics (Oxford, England)

|June 16, 2022

Summary

This summary is machine-generated.

Shepherd, a novel clustering method, accurately identifies true DNA barcodes from noisy sequencing data. This method significantly reduces spurious lineages, enabling higher resolution lineage tracking and more precise biological quantity estimation.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA barcodes are essential for tracking cell lineages in biological systems.
Next-generation sequencing introduces errors, complicating accurate barcode identification.
Existing methods struggle with the scale and error rates of barcode sequencing data.

Purpose of the Study:

To develop a robust method for correcting errors in DNA barcode sequences.
To improve the accuracy of lineage tracking using noisy sequencing data.
To enable more precise estimation of biological quantities from lineage data.

Main Methods:

Framed barcode error correction as a clustering problem.
Developed Shepherd, a novel clustering method utilizing k-mer indexing.
Incorporated a Bayesian statistical test with a substitution error rate.

Main Results:

Shepherd significantly outperforms state-of-the-art methods in accuracy.
Reduced spurious lineages by 10-150 times on synthetic data.
Demonstrated consistent improvements on empirical data, enabling higher resolution lineage tracking.

Conclusions:

Shepherd provides a significant advancement in DNA barcode error correction.
The method enhances the accuracy of lineage tracking and biological quantity estimation.
Enables detection of small effect mutations and improves understanding of evolutionary dynamics.