MOTIVATION: Genetic linkage analysis has made a huge contribution to the genetic mapping of Mendelian diseases. However, most previously available linkage analysis methods have limited applicability. Since parametric linkage analysis requires predefined model of inheritance with a fixed set of parameters, it is inapplicable without fully structured pedigree information. Furthermore, the analytical results are dependent on the specification of model parameters. While non-parametric linkage analysis can avoid these problems, the runs of homozygosity (ROH) mapping, a widely used non-parametric linkage analysis method, can only deal with recessive inheritance. The implementation of non-parametric linkage analyses capable of dealing with both dominant and recessive inheritance has been required. RESULTS: We have developed the Obelisc (Observational linkage scan), a flexibly applicable user-friendly non-parametric linkage analysis tool, which also provides an intuitive visualization of the analytical results. Obelisc is based on the SNP streak approach, which does not require any predefined inheritance model with parameters. In contrast to the ROH mapping, the SNP streak approach is applicable to both dominant and recessive traits. To illustrate the performance of Obelisc, we generated a pseudo-pedigree from the publicly available BioBank Japan Project genome-wide genotype dataset (n > 180 000). By applying Obelisc to this pseudo-pedigree, we successfully identified the regions with inherited identical-by-descent haplotypes shared among the members of the pseudo-pedigree, which was validated by the population-based haplotype phasing approach. AVAILABILITY AND IMPLEMENTATION: Obelisc is feely available at https://github.com/qsonehara/Obelisc as a python package with example datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics. AVAILABILITY AND IMPLEMENTATION: GREP is available at https://github.com/saorisakaue/GREP as a python source code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Rapid advances in high-throughput sequencing technologies have enabled more efficient acquisition of massive amount of multi-omics data. However, interpretation of the underlying relationships across multi-omics networks has not been fully succeeded, partly due to the lack of effective methods in visualization. To aid interpretation of the results from such multi-omics data, we here present Grimon (Graphical interface to visualize multi-omics networks), an R package that visualizes high-dimensional multi-layered data sets in three-dimensional parallel coordinates. Grimon enables users to intuitively and interactively explore their analyzed data, helping their understanding of multiple inter-layer connections embedded in high-dimensional complex data. Availability and implementation: Grimon is freely available at https://github.com/mkanai/grimon as an R package with example omics data sets. Supplementary information: Supplementary data are available at bioinformatics online.