MOTIVATION: The majority of genome analysis tools and pipelines require data to be decrypted for access. This potentially leaves sensitive genetic data exposed, either because the unencrypted data is not removed after analysis, or because the data leaves traces on the permanent storage medium. RESULTS: : We defined a file container specification enabling direct byte-level compatible random access to encrypted genetic data stored in community standards such as SAM/BAM/CRAM/VCF/BCF. By standardizing this format, we show how it can be added as a native file format to genomic libraries, enabling direct analysis of encrypted data without the need to create a decrypted copy. AVAILABILITY AND IMPLEMENTATION: The Crypt4GH specification can be found at: http://samtools.github.io/hts-specs/crypt4gh.pdf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Association studies based on SNP arrays and Next Generation Sequencing technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies. RESULTS: We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of linkage disequilibrium between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the patho-physiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx and provide further evidence supporting a functional link between them. AVAILABILITY AND IMPLEMENTATION: GePhEx is available at: https://gephex.ega-archive.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.