The Hjerling-Leffler lab is the leading analyst-lab for the Psychiatric Genomics Consortium (PGC) Functional Genomics (FG) group, which is dedicated to understanding the molecular and cellular mechanisms underlying psychiatric and neurological disorders. Recent advances in genome-wide association studies (GWAS) have identified thousands of genetic loci associated with disorders such as schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder. Despite these substantial genetic discoveries, translating these findings into meaningful biological insights and therapeutic targets remains a significant scientific challenge. Our project directly addresses this crucial gap by systematically integrating, analyzing, and interpreting extensive multi-omics datasets encompassing a broad spectrum of psychiatric conditions. By leveraging diverse molecular data modalities, including genomics, transcriptomics, epigenomics, and proteomics, we aim to dissect both shared and distinct molecular pathways and biological processes that underpin these disorders. To accomplish our objectives, we will develop, implement, and validate robust, standardized computational pipelines for data alignment, integration, quality control, and analysis. These pipelines will not only handle diverse publicly available datasets but also facilitate methodolocial cross-dataset comparisons and meta-analyses. This standardized methodology will significantly enhance the comparability of the different datasets, as well as increasing the reproducibility and transparency of our analyses, thereby promoting open and collaborative science within the global research community. Certain publicly available datasets utilized in our research, such as those from PsychENCODE, necessitate adherence to stringent data security standards, specifically ISO 27001 or ISO 27002, owing to the sensitive and confidential nature of human-derived genomic and transcriptomic data. PsychENCODE, for example, includes extensive genomic information from both control subjects and patients diagnosed with a wide range of psychiatric disorders, presenting potential privacy and ethical concerns that must be strictly managed. To address these requirements, the Bianca compute infrastructure, which is designed in compliance with ISO 27001, provides a secure, reliable, and dedicated environment to safely manage, store, process, and analyze sensitive datasets without compromising data integrity or patient confidentiality. Taking the limited computational resources into consideration, Bianca will serve as an extension to our already available computational infrastructure, which we will continue to utilize for all non-sensitive datasets. By integrating the secure environment provided by Bianca with our current infrastructure and comprehensive multi-omics analytical framework, this project aims to significantly advance our understanding of psychiatric and neurological disorders. Ultimately, our efforts seek to unravel the complex relationships between genetic discoveries and functional biological mechanisms, thereby contributing meaningful insights into the underlying causes and mechanisms of a wide of psychiatric disorders.