The mechanism of Lingze tablets in the treatment of benign prostatic hyperplasia based on network pharmacology and molecular docking technology.

Lingze tablets has been used as a drug in the treatment of kidney deficiency-dampnes shea-stasis type benign prostatic hyperplasia (BPH) in Traditional Chinese Medicine, and it was found effective for BPH treatment. We aimed to investigate the mechanism of the Lingze tablets in the treatment of BPH through the network pharmacology and molecular docking technology. The active compounds of Lingze tablets were retrieved from the TCMSP, BATMAN-TCM and ETCM databases. The ADME of active compounds was screened for Swiss target prediction, and the targets of the active compounds were predicted. DisGeNET, Genecards and OMIM were used to obtain the disease targets of BPH, and the targets of Lingze tablets in the treatment of BPH were obtained by venny2.1. String platform and cytoscape softwares were used to construct the PPI network. Go enrichment analysis and KEGG signal pathway analysis were analysed by mediascape. The 'component-target-pathway' networks diagram was constructed by the cytoscape software. Molecular docking was carried out by autodock software. Lingze tablets could serve as a drug for BPH treatment by regulating SRC, MAPK1, PIK3CA, JAK2 and other disease targets, intervening in biological processes such as cell migration, cell activity, cytokine secretion, protein phosphorylation, MAPK, transferase activity and PI3K/AKT signalling pathways.

DelInsCaller: An Efficient Algorithm for Identifying Delins and Estimating Haplotypes from Long Reads with High Level of Sequencing Errors.

Delins, as known as complex indel, is a combined genomic structural variation formed by deleting and inserting DNA fragments at a common genomic location. Recent studies emphasized the importance of delins in cancer diagnosis and treatment. Although the long reads from PacBio CLR sequencing significantly facilitate delins calling, the existing approaches still encounter computational challenges from the high level of sequencing errors, and often introduce errors in genotyping and phasing delins. In this paper, we propose an efficient algorithmic pipeline, named delInsCaller, to identify delins on haplotype resolution from the PacBio CLR sequencing data. delInsCaller design a fault-tolerant method by calculating a variation density score, which helps to locate the candidate mutational regions under a high-level of sequencing errors. It adopts a base association-based contig splicing method, which facilitates contig splicing in the presence of false-positive interference. We conducted a series of experiments on simulated datasets, and the results showed that delInsCaller outperformed several state-of-the-art approaches, e.g., SVseq3, across a wide range of parameter settings, such as read depth, sequencing error rates, etc. delInsCaller often obtained higher f-measures than other approaches; specifically, it was able to maintain advantages at ~15% sequencing errors. delInsCaller was able to significantly improve the N50 values with almost no loss of haplotype accuracy compared with the existing approach as well.