Ariadne

Ariadne deconvolves Synthetic Long Read (SLR) sequencing datasets using an assembly graph-based algorithm to assign reads to original long fragments and enable improved taxonomic classification and de novo assembly in metagenomic analyses.

Key Features:

• Assembly graph-based algorithm: Implements an assembly graph-based algorithm to deconvolve SLR sequencing datasets.
• Support for SLR technologies: Handles SLR techniques including UST’s TELL-Seq and Loop Genomics’ LoopSeq that combine 3′ barcoding with short-read sequencing.
• UMI ambiguity resolution: Addresses the lack of a direct one-to-one correspondence between long fragments and 3′ unique molecular identifiers (UMIs) through graph-based deconvolution.
• Single-species read-cloud extraction: Extracts single-species read-clouds from complex SLR datasets.
• Increased linkage resolution: Leverages SLR linkage extending from hundreds to tens of thousands of base pairs to enhance linkage information among reads.
• Downstream analysis improvement: Enhances taxonomic classification and de novo assembly in metagenomic datasets.

Scientific Applications:

• Taxonomic classification: Improves taxonomic classification in metagenomic studies by producing more coherent single-species read-clouds.
• De novo assembly: Enhances de novo assembly of metagenomes by increasing long-range linkage information between short reads.
• Complex community analysis: Facilitates analysis of complex microbial communities and multifaceted populations in metagenomes.

Methodology:

Uses an assembly graph-based algorithm to deconvolve SLR datasets and extract single-species read-clouds, addressing ambiguous assignments between long fragments and 3′ UMIs.