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Abstract The concept of U’s triangle, which revealed the importance of polyploidization in plant genome evolution, described natural allopolyploidization events in Brassica using three diploids [B. rapa (A genome), B. nigra (B), and B. oleracea (C)] and derived allotetraploids [B. juncea (AB genome), B. napus (AC), and B. carinata (BC)]. However, comprehensive understanding of Brassica genome evolution has not been fully achieved. Here, we performed low-coverage (2–6×) whole-genome sequencing of 28 accessions of Brassica as well as of Raphanus sativus [R genome] to explore the evolution of six Brassica species based on chloroplast genome and ribosomal DNA variations. Our phylogenomic analyses led to two main conclusions. (1) Intra-species-level chloroplast genome variations are low in the three allotetraploids (2~7 SNPs), but rich and variable in each diploid species (7~193 SNPs). (2) Three allotetraploids maintain two 45SnrDNA types derived from both ancestral species with maternal dominance. Furthermore, this study sheds light on the maternal origin of the AC chloroplast genome. Overall, this study clarifies the genetic relationships of U’s triangle species based on a comprehensive genomics approach and provides important genomic resources for correlative and evolutionary studies.

A new method to improve the efficiency of flanking sequence identification by genome walking was developed based on an expanded, sequential list of criteria for selecting candidate enzymes, plus several other optimization steps. These criteria include: step (1) initially choosing the most appropriate restriction enzyme according to the average fragment size produced by each enzyme determined using in silico digestion of genomic DNA, step (2) evaluating the in silico frequency of fragment size distribution between individual chromosomes, step (3) selecting those enzymes that generate fragments with the majority between 100 bp and 3,000 bp, step (4) weighing the advantages and disadvantages of blunt-end sites vs. cohesive-end sites, step (5) elimination of methylation sensitive enzymes with methylation-insensitive isoschizomers, and step (6) elimination of enzymes with recognition sites within the binary vector sequence (T-DNA and plasmid backbone). Step (7) includes the selection of a second restriction enzyme with highest number of recognition sites within regions not covered by the first restriction enzyme. Step (8) considers primer and adapter sequence optimization, selecting the best adapter-primer pairs according to their hairpin/dimers and secondary structure. In step (9), the efficiency of genomic library development was improved by column-filtration of digested DNA to remove restriction enzyme and phosphatase enzyme, and most important, to remove small genomic fragments (