Haplotype-resolved assembly of auto-polyploid genomes via combining Hi-C and gametic data.

Haplotype-resolved genome assembly plays a crucial role in understanding allele-specific functions. However, obtaining haplotype-resolved assembly for auto-polyploid genomes remains challenging. Existing methods can be classified into reference-based phasing, assembly-based phasing, and gamete binning. Nevertheless, there is a lack of cost-effective and efficient methods for haplotyping auto-polyploid genomes. In this study, we propose a novel phasing algorithm called PolyGH, which combines Hi-C and gametic data. We conducted experiments on tetraploid potato cultivars and divided the method into three steps. Firstly, gametic data was utilized to bin non-collapsed contigs, followed by merging adjacent fragments of the same type within the same contig. Secondly, accurate Hi-C signals related to differential genomic regions were acquired using unique k-mers. Finally, collapsed fragments were assigned to haplotigs based on combined Hi-C and gametic signals. Comparing PolyGH with Hi-C-based and gametic data-based methods, we found that PolyGH exhibited superior performance in haplotyping auto-polyploid genomes when integrating both data types. This approach has the potential to enhance haplotype-resolved assembly for auto-polyploid genomes.

CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations.

Genomic copy number variations (CNVs) are among the most important structural variations of genes found to be related to the risk of individual cancer and therefore they can be utilized to provide a clue to the research on the formation and progression of cancer. In this paper, an improved computational gene selection algorithm called CRIA (correlation-redundancy and interaction analysis based on gene selection algorithm) is introduced to screen genes that are closely related to cancer from the whole genome based on the value of gene CNVs. The CRIA algorithm mainly consists of two parts. Firstly, the main effect feature is selected out from the original feature set that has the largest correlation with the class label. Secondly, after the analysis involving correlation, redundancy and interaction for each feature in the candidate feature set, we choose the feature that maximizes the value of the custom selection criterion and add it into the selected feature set and then remove it from the candidate feature set in each selection round. Based on the real datasets, CRIA selects the top 200 genes to predict the type of cancer. The experiments' results of our research show that, compared with the state-of-the-art related methods, the CRIA algorithm can extract the key features of CNVs and a better classification performance can be achieved based on them. In addition, the interpretable genes highly related to cancer can be known, which may provide new clues at the genetic level for the treatment of the cancer.