Ariadne: Synthetic Long Read Deconvolution Using Assembly Graphs

AbstractBackgroundDe novo assemblies are critical for capturing the genetic composition of complex samples. Synthetic Long 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 SLR sequencing to genome assembly has demonstrated that barcoding-based technologies balance the tradeoffs between long-range linkage, per-base coverage, and costs. However, multiple long fragments may be associated with the same 3′ nucleotide-based unique molecular identifier (UMI). The lack of a 1:1 correspondence between a long fragment and a UMI, in conjunction with low sequencing depth, confounds the assignment of linkage between short-reads.ResultsWe introduce Ariadne, a novel SLR deconvolution algorithm based on assembly graphs, that can be used to extract single-species read-sets from a large SLR dataset. Ariadne deconvolution of SLR 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.ConclusionsIntegrating Ariadne into the post-processing pipeline for SLR technologies increases the quality of de novo assembly for complex populations, such as microbiomes.