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Updated: Jul 17, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Ariadne: synthetic long read deconvolution using assembly graphs.

Lauren Mak^1,2, Dmitry Meleshko^3,4, David C Danko^3,4

¹Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine of Cornell University, New York, USA. lam4003@med.cornell.edu.

|August 28, 2023

Summary

This summary is machine-generated.

Synthetic long read sequencing offers high resolution but struggles with read linkage. Ariadne, a new algorithm, deconvolutes these reads for better analysis of complex populations like metagenomes.

Keywords:

Assembly graphs Barcode deconvolution Metagenomics Synthetic long read

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Published on: January 25, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Synthetic long read sequencing (e.g., TELL-Seq, LoopSeq) links short reads using 3' barcoding for improved resolution.
A key challenge is the lack of a 1:1 mapping between long fragments and unique molecular identifiers, hindering accurate read linkage.

Purpose of the Study:

To introduce Ariadne, a novel algorithm for deconvoluting synthetic long reads.
To enable the extraction of single-species read-clouds from complex datasets.

Main Methods:

Development of Ariadne, an assembly graph-based deconvolution algorithm.
Application of Ariadne to synthetic long read datasets.

Main Results:

Ariadne effectively deconvolutes synthetic long reads by resolving linkage ambiguities.
The algorithm facilitates the isolation of single-species read-clouds.

Conclusions:

Ariadne improves taxonomic classification and de novo assembly for complex populations.
This method enhances the utility of synthetic long read sequencing for metagenomic studies.