MOTIVATION: The Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce Graph-KIR, a tool designed to estimate gene copy numbers and predict full-resolution (7-digit, encompassing both coding and non-coding sequence variations) from a whole genome sequencing (WGS) sample. RESULTS: Graph-KIR is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 99.2% accuracy in copy number estimation and high F1-score of allele typing: 91.79% at 7-digit resolution, 97.37% at 5-digit resolution, and 97.11% at 3-digit resolution. Graph-KIR outperforms existing tools such as Geny (96.39% F1-score), PING's WGS version (92.77% F1-score), and T1K (90.44% F1-score) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves better F1-score than Geny and PING at 7-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples. AVAILABILITY AND IMPLEMENTATION: The Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

BACKGROUND: Glucocorticoid-remediable aldosteronism (GRA) is a form of heritable hypertension caused by a chimeric fusion resulting from unequal crossing over between 11β-hydroxylase (CYP11B1) and aldosterone synthase (CYP11B2), which are two genes with similar sequences. Different crossover patterns of the CYP11B1 and CYP11B2 chimeric genes may be associated with a variety of clinical presentations. It is therefore necessary to develop an efficient approach for identifying the differences between the hybrid genes of a patient with GRA. RESULTS: We developed a long-read analysis pipeline named GRAde (GRA deciphering), which utilizes the nonidentical bases in the CYP11B1 and CYP11B2 genomic sequences to identify and visualize the chimeric form. We sequenced the polymerase chain reaction (PCR) products of the CYP11B1/CYP11B2 chimeric gene from 36 patients with GRA using the Nanopore MinION device and analyzed the sequences using GRAde. Crossover events were identified for 30 out of the 36 samples. The crossover sites appeared in the region exhibiting high sequence similarity between CYP11B1 and CYP11B2, and 53.3% of the cases were identified as having a gene conversion in intron 2. More importantly, there were six cases for whom the PCR products indicated a chimeric gene, but the GRAde results revealed no crossover pattern. The crossover regions were further verified by Sanger sequencing analysis. CONCLUSIONS: PCR-based target enrichment followed by long-read sequencing is an efficient and precise approach to dissecting complex genomic regions, such as those involved in GRA mutations, which could be directly applied to clinical diagnosis. The scripts of GRAde are available at https://github.com/hsu-binfo/GRAde .

Summary: Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries. Availability and implementation: DynaPho is freely available at http://dynapho.jhlab.tw/ . Contact: [email protected] or [email protected] . Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Exploring microRNA (miRNA) regulations and protein-protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein-protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer. AVAILABILITY AND IMPLEMENTATION: Mirin is freely available at http://mirin.ym.edu.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.