Sim3C: simulation of Hi-C and Meta3C proximity ligation sequencing technologies

Abstract

BackgroundChromosome conformation capture (3C) and Hi-C DNA sequencing methods have rapidly advanced our understanding of the spatial organization of genomes and metagenomes. Many variants of these protocols have been developed, each with their own strengths. Currently there is no systematic means for simulating sequence data from this family of sequencing protocols, potentially hindering the advancement of algorithms to exploit this new datatype.FindingsWe describe a computational simulator that, given simple parameters and reference genome sequences, will simulate Hi-C sequencing on those sequences. The simulator models the basic spatial structure in genomes that is commonly observed in Hi-C and 3C datasets, including the distance-decay relationship in proximity ligation, differences in the frequency of interaction within and across chromosomes, and the structure imposed by cells. A means to model the 3D structure of randomly generated topologically associating domains is provided. The simulator considers several sources of error common to 3C and Hi-C library preparation and sequencing methods, including spurious proximity ligation events and sequencing error.ConclusionsWe have introduced the first comprehensive simulator for 3C and Hi-C sequencing protocols. We expect the simulator to have use in testing of Hi-C data analysis algorithms, as well as more general value for experimental design, where questions such as the required depth of sequencing, enzyme choice, and other decisions can be made in advance in order to ensure adequate statistical power with respect to experimental hypothesis testing.

Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types

“…We simulated two chr21 Hi–C data sets using sim3C, which probabilistically generates reads based on simulated chromosomal interaction domain (CID) boundaries, similar to TADs [26]. The two data sets differ at one boundary, which is located at 39 Mb in one data set and 40 Mb in the other, but otherwise have identical CIDs (Additional file 1: Fig.…”

Z/I1 Hybrid Virulence Plasmids Carrying Antimicrobial Resistance genes in S. Typhimurium from Australian Food Animal Production

“…These examples add to a growing number of reports of MDR plasmid sharing between E. coli , Salmonella enterica and other members of the Enterobacteriaceae [48,49,50,51]. Genomic surveillance efforts are in their infancy in Australia, and it is likely that many more instances of plasmid sharing within the Enterobacteriaceae and perhaps other bacterial genera will become evident as surveillance becomes routine and metagenomic methodologies become more sophisticated [52,53]. At present, the Z/I1 hybrid plasmids described here are limited to these isolations from Australian Enterobacteriaceae , and any inferences we make may only be pertinent to these specific examples; however, it is likely variants of these plasmids are widely disseminated given the length of time since they were isolated both here, and from cattle and human sources [14,15].…”

Twelve quick steps for genome assembly and annotation in the classroom

“…The new method may be assessed in different ways depending on the context (e.g., by conducting simulations, applying the method to several real datasets, applying flexible parameter settings, and checking the underlying assumptions in practical examples). Therefore, simulations should not be limited to artificial datasets that correspond exactly to the assumptions underlying the new method as this would favor the new method [61,[95][96][97][98].…”