The Hi-C scaffolder GRAAL (Genome Re-Assembly Assessing Likelihood from 3D) is a probabilistic program that uses a Markov Chain Monte Carlo (MCMC) approach. stretches of bp, whether they be small-scale contigs or full chromosomes. Early studies using control datasets demonstrated that Hi-C can be used to scaffold and/or correct a wide range of eukaryotic DNA regions, i.e. When applied to genomics, Hi-C-based methods are sometimes referred to as proximity ligation approaches, as they quantify and exploit physical contacts between pairs of DNA segments in a genome to assess their collinearity along a chromosome, and the distance between the segments. The latter procedure derives from techniques aiming at recovering snapshots of the higher-order organization of a genome. Recent sequencing projects have typically relied on a combination of independently obtained data such as optical mapping, long-read sequencing, and chromosomal conformation capture (3C, Hi-C) to obtain large genome assemblies of high accuracy. However, there is as yet no systematic, reliable workflow of producing near-perfect genome assemblies of guaranteed optimal best quality without a considerable amount of empiric parameter adjustment and manual post-processing evaluation and correction. These limitations have been partially addressed thanks to active support from the community and competitions such as GAGE or the Assemblathon. Consequently, many currently available genomes still contain structural errors, as well as gaps that need to be bridged to reach a chromosome-level structure. In addition, long-read-based assemblies are associated with increased error rate among long reads, which can result in misassemblies. The development of long-read sequencing technology and accompanying assembly programs has considerably alleviated these difficulties, but some gaps remain nevertheless in genome scaffolds, notably at the level of long repeated/low-complexity DNA sequences. At the chromosome level, these programs often incorrectly orient DNA sequences or predict incorrect numbers of chromosomes. These assemblers efficiently generate overlapping sets of reads (i.e., contiguous sequences or contigs) but encounter difficulties linking these contigs together into scaffolds. Conventional assembly programs and pipelines often encounter difficulties to close gaps in draft genome assemblies introduced by regions enriched in repeated elements. Continuous developments in DNA sequencing technologies aim at alleviating the technical challenges that limit the ability to assemble sequence data into full-length chromosomes.
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