Tigmint: correct misassemblies using linked reads from large molecules
Creators: name: Jackman, Shaun D.
name: Vandervalk, Benjamin P.
name: Warren, Rene L.
name: Mohamadi, Hamid
name: Chu, Justin
name: Yeo, Sarah
name: Coombe, Lauren
name: Joerg Bohlmann
name: Jones, Steven J.M.
name: Inanc Birol
Publisher: name: F1000Research
Year: 2017

description: Long-read sequencing technologies have greatly improved assembly contiguity, but at a cost roughly ten times that of short-read sequencing technology. For population studies and when sequencing large genomes, such as conifer genomes and other economically important crop species, this cost may be prohibitive. The 10x Genomics Chromium technology generates linked reads from large DNA molecules at a cost comparable to standard short-read sequencing technologies. Whereas paired-end sequencing gives two reads from a small DNA fragment, linked reads yield roughly a hundred reads from molecules with a typical size of 10 to 100 kilobases. Linked reads indicate which reads were derived from the same DNA molecule, and so should be in close proximity in the underlying genome. Linked reads have been used previously to phase diploid genomes using a reference, <i>de novo</i> assemble complex genomes in the gigabase scale, and further scaffold draft assemblies.<br><br>In <i>de novo</i> sequencing projects, it is challenging yet important to measure the correctness of the resulting assemblies. Linked reads from technologies such as Chromium offer an opportunity to algorithmically address this problem. Here we introduce a software tool, Tigmint, to identify misassemblies using linked reads. The reads are first aligned to the assembly, and the extents of the large DNA molecules are inferred from the alignments of the reads. The physical coverage of the large molecules is more consistent and less prone to coverage dropouts than that of the short read sequencing data. Atypical drops in physical molecule coverage, less than the median minus 1.5 times the inter-quartile range, reveal possible misassemblies. Clipped alignments of the first and last reads of a molecule are used to refine the coordinates of the misassembly with base-pair accuracy.<br><br>No software tool currently exists for the specific purpose of identifying misassemblies using linked reads. The tool Long Ranger by 10x Genomics detects structural variants, which is a similar task. It requires however a reference genome assembled in fewer than 500 contigs, whereas a <i>de novo</i> assembly is often more fragmented. Tigmint addresses specifically the unaddressed problem of identifying misassemblies using linked reads.<br><br>Assemblies of short read sequencing data are easily confounded by repetitive sequence larger than the fragment size of the sequencing library. When the size of a repeat exceeds the library fragment size, the contig comes to an end in the best case, or results in misassembled sequence in the worst case. Tigmint is particularly useful in correcting these misassemblies when the initial assembly of the Illumina paired-end reads did not employ the barcodes of the linked reads, and this rich source of evidence is yet untapped.<br><br>Misassemblies not only complicate downstream analyses, but also limit the contiguity of the assembly, when incorrectly assembled sequences prevent joining their adjacent and correctly assembled sequences. To demonstrate the utility of Tigmint, we assemble the six megabase mitochondrial genome of Sitka spruce (<i>Picea sitchensis</i>) from 10x Genomics Chromium data using ABySS 2.0, identify and correct misassemblies using Tigmint, and scaffold using ARCS. Tigmint identifies 16 structural misassemblies in this case. After scaffolding with ARCS, the mitochondrial genome is assembled in 12 scaffolds larger than 100 kbp, with an N50 of 493 kbp. We plan to apply this method to assemble the twenty gigabase nuclear genome of Sitka spruce. Chromium reads permit cost-effective assembly of large genomes with high-throughput, short-read sequencing technology, while also providing large-molecule scaffolding data.