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ISCBacademy is an online webinar series including the ISCB COSI, COVID webinars, Indigenous Voices and practical tutorials. We aim to inspire, connect, and communicate the science while providing a hands-on experience accessing and using newly developed bioinformatics tools while ensuring best practices for rigour and reproducibility.
January 28, 2025 at 11:00 AM EST
Neurodegenerative (e.g. PD - Parkinson’s disease), immunological (e.g IBD - Inflammatory Bowl Disease, MS - Multiple Sclerosis, RA - Rheumatoid Arthritis) and recent COVID19 diseases are quite complex in their aetiology. In order to stratify and discover the signatures, biomarkers for early diagnosis, we need high quality clinical cohort studies. This talk will focus on the clinical and translational medicine informatics approaches developed to build up cohorts and to capture the clinical data using state-of-the-art electronic Case Report Forms (eCRFs) encoded with standard ontologies and controlled terminologies. Secure management and efficient integration of clinical, associated molecular data (multi omics - genomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiome data), imaging and sensor/mobile data. Interoperability of this heterogeneous data in content and format is another challenge. It involves, mammoth task of data curation, harmonisation and FAIRification (making data Findable, Accessible, Interoperable and Reusable) to facilitate the cross-study analysis. Application of statistical and Machine Learning (ML) methods to analyse multi-layered data to stratify the patients into different subgroups based on disease severity and progression and identification of biomarkers representing each subgroup. In addition, application of knowledge management methods, disease maps, to discover the mechanistic models and co-morbidities of these complex diseases.
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February 19, 2025 at 6:00 PM CET
This tutorial is designed to equip participants with foundational skills in using the Nextflow language for bioinformatics workflows. Attendees will gain hands-on experience in the basic concepts in designing, building, and executing workflows efficiently. Concrete applications will be discussed, using variant calling with the Genome Analysis Toolkit (GATK) as a case study for the walkthrough. By the end of the tutorial, participants will be proficient in the basic principles and syntax of Nextflow, will understand how to execute workflows through the Command Line Interface (CLI), and how to troubleshoot common issues. Additionally, the workshop will introduce a few next-step concepts that are essential for scaling and optimising bioinformatics pipelines.
Learning Objectives
This tutorial aims to build basic proficiency in the following areas:
Nextflow language:
- understand nextflow principles
- know the basic syntax of the code
- use the core components
- apply these concepts to build a simple multi-step workflow
- understand the next-step concepts such as operators and channel factories
Command Line Interface execution:
- launch a Nextflow workflow locally
- find the pipeline outputs (results)
- interpret the log outputs
- troubleshoot basic issues
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February 25, 2025 at 9:00 AM EST
In many environments, microbes are constantly facing evolutionary pressure from different agents. One important pressure is asserted by the immune system. Especially for viruses, the variants have to adapt to the adaptive immune system to establish a chronic infection.
HIV is one of the viruses, where this adaptation process has been well studied. With our research, we have contributed computational methods to model the adaptation of the virus to the immune system pressure. I will give an overview of the challenges, the early models [1] as well as recent updates on how to model this efficiently [2]. Furthermore, I will show that correcting for the founder effects leads to more accurate predictions and point out implications for bacterial analyses like resistance prediction for Mycobacterium tuberculosis.
1. Carlson, J., Du, V., Pfeifer, N. et al. Impact of pre-adapted HIV transmission. Nat Med 22, 606–613 (2016)
2. Hake, A., Germann, A., de Beer, C. et al. Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models, PLOS Computational Biology, 19(12): e1010355 (2023)
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March 20, 2025 at 9:00 AM EST
Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in contemporary scientific research, enabling the revelation of cellular heterogeneity and the discovery of novel insights. However, the development of a great variety of tools for single-cell data analysis has introduced complexity, making it challenging to identify critical steps in the analysis workflow and determine the most effective methods for specific study objectives.
This tutorial aims to guide participants in the design of a scRNA-seq experiment and the effective management and analysis of data, starting from count matrices. It serves as a bridge for both experimental and computational biologists, providing hands-on experience and essential skills in scRNA-seq analysis.
Designed for Master's or PhD students and researchers in bioinformatics, experimental and computational biology, and medical informatics, this comprehensive tutorial employs the Seurat package in R/RStudio. Tailored for beginners and individuals with limited scRNA-seq experience, the session aims to establish a solid foundation in using tools for scRNA-seq analysis by guiding participants through various steps of typical workflows using example datasets.
Attendees will gain proficiency in navigating the intricacies of scRNA-seq by acquiring valuable skills, including:
• understanding motivations for selecting scRNA-seq, distinguishing it from other sequencing methods;
• addressing challenges in designing and analyzing scRNA-seq experiments;
• mastering key analysis stages to transform data into biologically meaningful insights;
• calculating and evaluating quality control metrics at different workflow stages;
• conducting data exploration, normalization, and dimensionality reduction for scRNA-seq datasets;
• employing clustering techniques to identify distinct cell types;
• integrating scRNA-seq data from multiple samples.
To further enhance the learning experience, all course materials - including slides, datasets, and script examples for hands-on exercises - will be freely accessible to attendees on a dedicated web page/git repository.
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