Ariadne: Barcoded Linked-Read Deconvolution Using de Bruijn Graphs

Abstract

AbstractDe novo assemblies are critical for capturing the genetic composition of complex samples. Linked-read sequencing techniques such as 10x Genomics’ Linked-Reads, UST’s TELL-Seq, Loop Genomics’ LoopSeq, and BGI’s Long Fragment Read combines 3′ barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. The application of linked-read sequencing to genome assembly has demonstrated that barcoding-based technologies balance the tradeoffs between long-range linkage, per-base coverage, and costs. Linked-reads come with their own challenges, chief among them the association of multiple long fragments with the same 3′ barcode. The lack of a unique correspondence between a long fragment and a barcode, in conjunction with low sequencing depth, confounds the assignment of linkage between short-reads.ResultsWe introduce Ariadne, a novel linked-read deconvolution algorithm based on assembly graphs, that can be used to extract single-species read-sets from a large linked-read dataset. Ariadne deconvolution of linked-read clouds increases the proportion of read clouds containing only reads from a single fragment by up to 37.5-fold. Using these enhanced read clouds in de novo assembly significantly improves assembly contiguity and the size of the largest aligned blocks in comparison to the non-deconvolved read clouds. Integrating barcode deconvolution tools, such as Ariadne, into the postprocessing pipeline for linked-read technologies increases the quality of de novo assembly for complex populations, such as microbiomes. Ariadne is intuitive, computationally efficient, and scalable to other large-scale linked-read problems, such as human genome phasing.AvailabilityThe source code is available on GitHub: https://github.com/lauren-mak/Ariadne