Gunter Worm (cougarsphere7)

We also provide the list of unstable positions for converting between the two most commonly used builds GRCh37 and GRCh38. Pre-excluding SNVs at these positions, prior to conversion, results in SNVs that are stable to conversion. This simple procedure gives the same final list of stable SNVs as applying the algorithm and subsequently removing variants at unstable positions. This work highlights the care that must be taken when converting SNVs between genome builds and provides a simple method for ensuring higher confidence converted data. Unstable positions and algorithm code, available at https//github.com/cathaloruaidh/genomeBuildConversion.It is pivotal and remains challenge for cancer precision treatment to identify the survival outcome interactions between genes, cells and drugs. Here, we present siGCD, a web-based tool for analysis and visualization of the survival interaction of Genes, Cells and Drugs in human cancers. siGCD utilizes the cancer heterogeneity to simulate the manipulated gene expression, cell infiltration and drug treatment, which overcomes the data and experimental limitations. To illustrate the performance of siGCD, we identified the survival interaction partners of EGFR (gene level), T cells (cell level) and sorafenib (drug level), and our prediction was consistent with previous reports. Moreover, we validate the synergistic effect of regorafenib and glyburide, and found that glyburide could significantly improve the regorafenib response. These results demonstrate that siGCD could benefit cancer precision medicine in a wide range of advantageous application scenarios including gene regulatory network construction, immune cell regulatory gene identification, drug (especially multiple target drugs) response biomarker screening and combination therapeutic design. Exploring the relationship between human proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment of diseases. The human phenotype ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human diseases. However, the current HPO annotations of proteins are not complete. Thus, it is important to identify missing protein-phenotype associations. We propose HPOFiller, a graph convolutional network (GCN)-based approach, for predicting missing HPO annotations. HPOFiller has two key GCN components for capturing embeddings from complex network structures 1) S-GCN for both protein-protein interaction (PPI) network and HPO semantic similarity network to utilize network weights; 2) Bi-GCN for the protein-phenotype bipartite graph to conduct message passing between proteins and phenotypes. The core idea of HPOFiller is to repeat run these two GCN modules consecutively over the three networks, to refine the embeddings. Empirical results of extremely stringent evaluation avoiding potential information leakage including cross-validation and temporal validation demonstrates that HPOFiller significantly outperforms all other state-of-the-art methods. In particular, the ablation study shows that batch normalization contributes the most to the performance. The further examination offers literature evidence for highly ranked predictions. Finally using known disease-HPO term associations, HPOFiller could suggest promising, unknown disease-gene associations, presenting possible genetic causes of human disorders. https//github.com/liulizhi1996/HPOFiller. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online.Mineralization of the fetal mammalian skeleton requires a hypercalcemic gradient across the placenta from mother to fetus. However, the mechanisms responsible for maintaining the placental transport of calcium remain poorly understood. This study aimed to identify calcium and vitamin D regulatory pathway components in ovine endometria and placentae across gestation. Suff