Characterising genome architectures using genome decomposition analysis

The haplotype-resolved telomere-to-telomere carnation (Dianthus caryophyllus) genome reveals the correlation between genome architecture and gene expression

Carnation (Dianthus caryophyllus) is one of the most valuable commercial flowers, due to its richness of color and form, and its excellent storage and vase life. The diverse demands of the market require faster breeding in carnations. A full understanding of carnations is therefore required to guide the direction of breeding. Hence, we assembled the haplotype-resolved gap-free carnation genome of the variety 'Baltico', which is the most common white standard variety worldwide. Based on high-depth HiFi, ultra-long nanopore, and Hi-C sequencing data, we assembled the telomere-to-telomere (T2T) genome to be 564 479 117 and 568 266 215 bp for the two haplotypes Hap1 and Hap2, respectively. This T2T genome exhibited great improvement in genome assembly and annotation results compared with the former version. The improvements were seen when different approaches to evaluation were used. Our T2T genome first informs the analysis of the telomere and centromere region, enabling us to speculate about specific centromere characteristics that cannot be identified by high-order repeats in carnations. We analyzed allele-specific expression in three tissues and the relationship between genome architecture and gene expression in the haplotypes. This demonstrated that the length of the genes, coding sequences, and introns, the exon numbers and the transposable element insertions correlate with gene expression ratios and levels. The insertions of transposable elements repress expression in gene regulatory networks in carnation. This gap-free finished T2T carnation genome provides a valuable resource to illustrate the genome characteristics and for functional genomics analysis in further studies and molecular breeding.

MeShClust v3.0: high-quality clustering of DNA sequences using the mean shift algorithm and alignment-free identity scores

Background

Tools for accurately clustering biological sequences are among the most important tools in computational biology. Two pioneering tools for clustering sequences are CD-HIT and UCLUST, both of which are fast and consume reasonable amounts of memory; however, there is a big room for improvement in terms of cluster quality. Motivated by this opportunity for improving cluster quality, we applied the mean shift algorithm in MeShClust v1.0. The mean shift algorithm is an instance of unsupervised learning. Its strong theoretical foundation guarantees the convergence to the true cluster centers. Our implementation of the mean shift algorithm in MeShClust v1.0 was a step forward. In this work, we scale up the algorithm by adapting an out-of-core strategy while utilizing alignment-free identity scores in a new tool: MeShClust v3.0.

Results

We evaluated CD-HIT, MeShClust v1.0, MeShClust v3.0, and UCLUST on 22 synthetic sets and five real sets. These data sets were designed or selected for testing the tools in terms of scalability and different similarity levels among sequences comprising clusters. On the synthetic data sets, MeShClust v3.0 outperformed the related tools on all sets in terms of cluster quality. On two real data sets obtained from human microbiome and maize transposons, MeShClust v3.0 outperformed the related tools by wide margins, achieving 55%–300% improvement in cluster quality. On another set that includes degenerate viral sequences, MeShClust v3.0 came third. On two bacterial sets, MeShClust v3.0 was the only applicable tool because of the long sequences in these sets. MeShClust v3.0 requires more time and memory than the related tools; almost all personal computers at the time of this writing can accommodate such requirements. MeShClust v3.0 can estimate an important parameter that controls cluster membership with high accuracy.

Conclusions

These results demonstrate the high quality of clusters produced by MeShClust v3.0 and its ability to apply the mean shift algorithm to large data sets and long sequences. Because clustering tools are utilized in many studies, providing high-quality clusters will help with deriving accurate biological knowledge.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08619-0).