Improving Illumina assemblies with Hi‐C and long reads: An example with the North African dromedary

Researchers have assembled thousands of eukaryotic genomes using Illumina reads, but traditional mate‐pair libraries cannot span all repetitive elements, resulting in highly fragmented assemblies. However, both chromosome conformation capture techniques, such as Hi‐C and Dovetail Genomics Chicago libraries and long‐read sequencing, such as Pacific Biosciences and Oxford Nanopore, help span and resolve repetitive regions and therefore improve genome assemblies. One important livestock species of arid regions that does not have a high‐quality contiguous reference genome is the dromedary (Camelus dromedarius). Draft genomes exist but are highly fragmented, and a high‐quality reference genome is needed to understand adaptation to desert environments and artificial selection during domestication. Dromedaries are among the last livestock species to have been domesticated, and together with wild and domestic Bactrian camels, they are the only representatives of the Camelini tribe, which highlights their evolutionary significance. Here we describe our efforts to improve the North African dromedary genome. We used Chicago and Hi‐C sequencing libraries from Dovetail Genomics to resolve the order of previously assembled contigs, producing almost chromosome‐level scaffolds. Remaining gaps were filled with Pacific Biosciences long reads, and then scaffolds were comparatively mapped to chromosomes. Long reads added 99.32 Mbp to the total length of the new assembly. Dovetail Chicago and Hi‐C libraries increased the longest scaffold over 12‐fold, from 9.71 Mbp to 124.99 Mbp and the scaffold N50 over 50‐fold, from 1.48 Mbp to 75.02 Mbp. We demonstrate that Illumina de novo assemblies can be substantially upgraded by combining chromosome conformation capture and long‐read sequencing.     

CellProfiler Analyst: data exploration and analysis software for complex image-based screens

Background  

Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment.