Whole Genome Mapping with Feature Sets from High-Throughput Sequencing Data

Abstract

A good physical map is essential to guide sequence assembly in de novo whole genome sequencing, especially when sequences are produced by high-throughput sequencing such as next-generation-sequencing (NGS) technology. We here present a novel method, Feature sets-based Genome Mapping (FGM). With FGM, physical map and draft whole genome sequences can be generated, anchored and integrated using the same data set of NGS sequences, independent of restriction digestion. Method model was created and parameters were inspected by simulations using the Arabidopsis genome sequence. In the simulations, when ~4.8X genome BAC library including 4,096 clones was used to sequence the whole genome, ~90% of clones were successfully connected to physical contigs, and 91.58% of genome sequences were mapped and connected to chromosomes. This method was experimentally verified using the existing physical map and genome sequence of rice. Of 4,064 clones covering 115 Mb sequence selected from ~3 tiles of 3 chromosomes of a rice draft physical map, 3,364 clones were reconstructed into physical contigs and 98 Mb sequences were integrated into the 3 chromosomes. The physical map-integrated draft genome sequences can provide permanent frameworks for eventually obtaining high-quality reference sequences by targeted sequencing, gap filling and combining other sequences.

Genomic Resources of Broomcorn Millet: Demonstration and Application of a High-throughput BAC Mapping Pipeline

“…If overlapping BAC clones appear in the same row or column pool, we can assign them to wells easily and correctly. Also If two overlapping BAC clones appear in a different row or column pool, we can rectify them by our previous method [17]. However, if more than two overlapping BAC clones appear in a different row or column pool, our method will lter out potential BESs, so that the BESs of the intersection well will be absent.…”

Genomic resources of broomcorn millet: demonstration and application of a high-throughput BAC mapping pipeline

“…If overlapping BAC clones appear in the same row or column pool, we can assign them to wells easily and correctly. Also, if two overlapping BAC clones appear in a different row or column pool, we can rectify them by our previous method [ 32 ]. However, if more than two overlapping BAC clones appear in a different row or column pool, our method will filter out potential BESs, so that the BESs of the intersection well will be absent.…”

Genomic Resources of Broomcorn Millet: Demonstration and Application of a High-throughput BAC Mapping Pipeline

“…The BAC clone overlapping rate in the superpool is the most important factor. When two same BAC clones appear in different rows and columns, we can filter them by our previous method [22].…”

“…The BAC clone overlapping rate in the superpool is the most important factor. When two same BAC clones appear in different rows and columns, we can filter them by our previous method [22]. However, if there are more than two same BAC clones that all of them contain in a row or column pool, our method will filter out all potential BESs, so that the BESs of the intersection well will absent.…”