Posters - Schedules

There are no instruction manuals for starting a new bioinformatics core, and often we need to learn things the hard way or by trial-and-error. Here, I will describe some lessons learned from building and running a core that I would not have considered ahead of time. This includes the compounding interest from consistent directory structure, the importance of a utilities package, the four different types of documentation needed to cover relevant audiences and time scales of a project, the benefits of using a project management system with an API, and ways of dealing with the scaling problem where a core’s resources cannot (for whatever reason) necessarily grow proportionally with the institution’s need.

Bioinformatics core facilities (BCF) play an essential role in enabling research in life sciences. As deep learning and high-throughput sequencing methodologies are increasingly applied to analyse molecular data, there is a growing need for highly specialised services from BCFs and their supporting teams.

BCF scientists face many career progression challenges, often hampered by the lack of formal education as they transition from a research focus to a service focus and management role

Several efforts have emerged to strengthen management-related competencies for BCFs. Since 2017 EMBL-EBI runs a yearly BCF management course, and biannually since 2019, an EMBO Research to Service practical course takes place. These events have reached over 100 participants delving into topics such as BCF strengths and limitations, financial sustainability, project management, estimation of human/computing resources, communication, and networking within BCFs and their associates/users, and the courses keep evolving according to emerging needs. The participants enthusiastically apply the gained knowledge with their teams and beyond, becoming BCF management trainers themselves and taking these courses to other regions.

Diverse efforts are needed to support the BCF community of practice. The training targeted toward this community is essential to continue enabling research and development within the life sciences.

Next Generation Sequencing (NGS) has pivotal role in healthcare development. We have built an integrated platform that consists of basic NGS analysis pipelines which can be utilised by any user with basic computational skills. Our pipelines are compatible with all available sequencing platforms and supports different data input formats. We have incorporated data resources from more than 8 publicly available databases that includes GEO, SRA, MGA, ReMap, ENCODE. The healthcare-react platform is made user friendly, cost effective with high level of cloud computing environment to reduce time. The platform is built to perform basic as well as advanced analysis that includes interactive gene expression plots, motif discovery, allele specific analysis, assessment of reproducibility and machine learning based approach, anatomogram and demographics. User can build his own pipeline based on different tools available for each step. We have incorporated our platform ‘HUBIO’ that let user to get variant or gene related literature data from PubMed database. With this platform we aim to improve clinical diagnostics and research for rare diseases and common genetic variants. In future, we aim to level up our platform by integrating different NGS pipelines for variant interpretation and linking genes to targets and disease for drug repurposing.

Chromatin immunoprecipitation sequencing (ChIP-seq) is primarily used to identify transcription factors and chromatin proteins. We have built a highly automated, user friendly platform with effective use of high computing environment for ChIP-Seq data analysis which can be utilised by any user with basic computational skills. Our platform has integrated data resources for ChIP-Seq from more than 8 publicly available databases that includes GEO, SRA, MGA, ReMap, ENCODE. Our platform supports different input formats, and the user have an option to build his own pipeline by choosing from different tools available in the platform. The platform performs basic as well as advanced analysis that includes motif discovery, allele specific analysis, assessment of reproducibility and machine learning based tools. Platform provides peak with bam comparisons to be visualized with interactive heatmaps. Results generated can be saved, compared, shared, and downloaded. Demo data is provided to help user to test run and understand the platform processing and results. We have also integrated ATAC-Seq. With this platform we aid in improving the transcriptional regulation studies. In future, we will include other possible pipelines and features in the platform such as Histone-Seq, DNase-Seq, chromatin state segmentation, enhancer target prediction, snps effects on TF binding, scChIP-Seq.

The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we develop a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully impute high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identify the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.

Whole genome sequencing (WGS) and whole exome sequencing (WES) techniques are becoming famous in diagnostics and research. We have built fast, efficient, and cost-effective platform for WGS and WES analysis. Based on the data and research purpose user can switch between the two pipelines available in our platform. We made our platform user friendly with high level of cloud computing environment to reduce time. Data processing have been boosted by using large instances that are generated at the running time based on the size of input data making it cost effective. Healthcare-react platform is compatible with all available sequencing platforms. For annotation of variants all plugins from VEP and SnpEff included. Sample size of 70Gb with 60X coverage was processed in less than a day. We have included ACMG guidelines for variant interpretation and ACGS, ClinGen and AMP guidelines will be included shortly. Population databases like Kovariome (Korean variant analysis) are integrated. Interactive visualizations such as anatomogram and demographics, variant related literature data were provided as built-in function. our healthcare-react platform can efficiently do genome analysis and variant interpretation which will aid in identifying the underlying cause of various diseases and accelerate the research for identifying variants for various diseases.

Despite the immense popularity of RNA-seq, the large-scale data analyses harbours challenges. We have developed a pipeline that transforms RNA-seq analysis – gives the user the flexibility to create their own pipelines- choose from a wide range of algorithms and software and bring any format of input data across any species to the platform. The user can also choose the speed of the analysis. We offer the unique option of integration of a user’s analysis with publicly available transcriptomics data, for cross validation, as well as with their own previous project. We associate genes of interest with protein-protein interactions, relevant PubMed records, non-coding RNA, link regulatory events such as differential splicing and gene fusion analysis to diseases, facilitating more conclusive genotype-phenotype relationship predictions. Also, subnetworks-oriented enrichment analysis, visualization of gene expression patterns, Co-expression module analysis – which gives us idea about systems-level functionality of genes are included to provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. ML prediction of biomarkers can be made from transcriptomics data for any given disease. With specialized tools merging and integrating we yield unprecedented insights into the complex biology of an organism and addresses previously unresolved questions.

While cost-effective high-throughput technologies by NGS provide an increasing amount of data, the analyses of single omics data seldom provide causal relations. Multi-omics data integration strategies including genomes, epigenomes, transcriptomes, proteomes, and metabolomes offer more opportunities to understand the underlying biology of complex diseases, such as cancer. However, multi-omics can only benefit cancer and disease research by linking and graphically implementing results in an integrated manner, rather than simply listing each of the single-omics data. Integrated implementation of multi-omics results for patients needs to support intuitive observation by physician scientists and researchers. We have built a platform that integrates various multi-omics data such as Genome, Transcriptome, Proteome, Epigenome, and others in cancer patients and allows them to visually check their results. It can also link basic clinical information with multi-omics to help identify various mechanisms and causes of cancer.