The ability of these methods to produce pseudo-molecules also was tested, as reported in Popseq and Allmaps. Methods used to anchor whole-genome shotgun (WGS) assemblies on genomes have been investigated using several genetic maps to estimate assembly quality, as implemented in MetaMap. Moreover, the physical distance between markers can be very high in genomic regions where the recombination rate is low, which makes it difficult to anchor or orientate scaffolds located in those regions. However, missing data or genotyping errors cause map inaccuracies. Genetic map construction takes advantage of sequence-based genotyping (SBG), genotyping-by-sequencing, and RAD-seq libraries to obtain ultra-dense genetic linkage maps. Genetic maps allow the construction of pseudo-molecules by anchoring the assembly on linkage groups that correspond to the chromosomes. More recently, the release of the Irys system from BioNano Genomics provided new opportunities to improve the quality and the contiguity of genome assemblies. ![]() For Amborella, this allowed the reordering and super-scaffolding of the draft assemblies and increased their contiguity (N50 increased from 4.9 to 9.3 Mb). Optical maps were used to assemble the Amborella and goat genomes. WGP has been used successfully to build physical maps of several plant genomes such as those of wheat and tobacco. In the WGP method, pooled BAC DNA is digested by a restriction enzyme and after amplification, Illumina technology is used to obtain sequence tags (typically 50 nucleotide sequences flanking the restriction sites). Recently, the Whole Genome Profiling (WGP™) approach was developed by Keygene NV (Wageningen, The Netherlands) to create an accurate sequence-based physical map starting from a bacterial artificial chromosome (BAC) library. Although, this strategy is time consuming and expensive, it remains the best option for high quality genome sequencing of large and complex (polyploid) genomes such as the wheat genome. Historically, physical maps have been used for large genome sequencing projects to order clones and perform clone-by-clone sequencing, which reduces the complexity of the assembly by sequencing single or pooled clones. ![]() Currently, several different types of genome maps can be produced to drive or improve assemblies including physical maps, optical maps, and genetic maps. Genome maps can also help in detecting assembly errors by revealing discrepancies between the map and the assembly and can provide independent information for evaluating genome assembly quality. To decrease the number of false links, scaffolder programs require a cutoff for the minimum number of read pairs (or long reads) that validate a contigs junction as a consequence, low-covered contigs are overlooked for scaffold building.Īccess to a genome map is a great advantage in obtaining a high-quality genome assembly. This is often the case in large and complex genomes where repetitive elements are large and cover a large fraction of the genome. However, during the alignment step, the presence of repeated sequences creates multiple assembly solutions, which generally causes ambiguities that scaffolder programs cannot untangle. Typically, 1 to 20 kb libraries are used consecutively during the scaffolding step, which allows repetitive regions of various sizes to be spanned. The efficiency of the scaffolding depends mainly on the diversity and fragment size of the input reads libraries and on the size and quality of the long reads. Several commonly used scaffolding programs have been published in the last decade. During the second step, end sequences of large fragments (>1 kb) or long reads are aligned to the contigs and the alignment information is used to link contigs into scaffolds. The genome assembly process usually involves four main steps: reads assembly into contiguous sequences (contigs), linking of contigs into larger gap-containing sequences (scaffolds), gap closing to fill gaps generated by the scaffolding, and anchoring onto a genetic map to build the final pseudo-molecules. ![]() Technical advances and cost reduction in genome sequencing have allowed the completion of numerous genome sequencing projects based on whole-genome shotgun fragments using high-throughput sequencing data and the assembly of these data.
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