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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.
November 4, 2024 at 2:00 PM EST
As macromolecular structures available through the Protein Data Bank (PDB) archive continue to grow in complexity and size, traditional text data formats like PDBx/mmCIF and the legacy PDB file format are becoming increasingly inefficient for transfer and parsing. To support scalable data analysis, binary formats and compression techniques are now essential.
Join our one-hour workshop to future-proof your data analysis with BinaryCIF, a fully interchangeable yet drastically more efficient flavor of the PDBx/mmCIF format. BinaryCIF not only boosts storage efficiency, but also substantially improves parsing speed, making it ideal for large-scale analyses. BinaryCIF is supported by resources such as RCSB PDB, PDBe, and AlphaFold DB.
This webinar will benefit bioinformaticians, data scientists, and structural biologists who want to
• Understand the basics of the PDBx/mmCIF schema
• Access BinaryCIF files and related APIs on RCSB.org
• Programmatically consume BinaryCIF data and convert between formats
• Compute archive-wide statistics across the entire PDB
• Gain hands-on experience with our Python parser
An institutional email address for registration is preferred.
You will receive confirmation and a Zoom link by email before the event.
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November 7, 2024 at 3:00 PM CET
This tutorial provides an introduction to genome-wide association study (GWAS) and genotype imputation, key methodologies in the field of computational genomics.
GWASes have revolutionized our investigation of the genetic basis of Mendelian and complex diseases as well as that of human traits by systematically examining millions of genetic variants across the entire genome. In recent years GWASes have been empowered by genotype imputation, a statistical technique which allows to accurately infer untyped genotypes and dramatically increase the number of genomic markers to test. Despite the usage of GWAS and genotype imputation in everyday genomic workflows, limited resources are available to understand the principles of genetic association analysis and perform genomic scans on imputed data.
The tutorial covers the fundamental principles of GWAS and genotype imputation in the context of human genetics. It offers a comprehensive overview of the association analysis on genetic data, which turns out to be effective not only on human datasets: genetic association tests are totally applicable and transferable to any other organisms, in which genomic positions are tested against a phenotype of interest to unravel the underlying genetic causes associated with a specific trait.
The tutorial addresses the following topics:
•introduction to genetic data from genotyping and sequencing technologies;
•the principal statistical methodologies to perform GWAS;
•quality controls and filtering options, on both variants (e.g., minor allele frequency) and samples (e.g., relatedness and ancestry);
•introduction to genotype imputation: main methods, popular tools and reference panels;
•solving the problem of missingness in genetic data with genotype imputation;
•perform a GWAS on typed and imputed data
Through a step-by-step approach, participants will gain both theoretical and practical insights into the implementation of a pipeline to perform GWASes with genotype imputation. Together with a notional explanation, the attendees will have the chance to take part in three hands-on sessions, covering:
•quality control and filtering of genetic data;
•data handling and genotype imputation;
•GWAS and results visualization;
The hands-on sessions will be performed on a publicly available and widely-used genetic dataset from the 1000 Genomes Project. The whole workflow will be carried out by using state-of-the-art toolsets recognized by the scientific community and above all easily understandable and accessible to beginners, such as plink and bcftools for quality controls and association studies and the Michigan and TOPMed imputation servers for the genotype imputation. Data visualization will be performed in the R statistical environment.
By the end of this tutorial, attendees will possess a general overview of the main concepts regarding genome-wide association studies and genotype imputation methodologies, boosting their knowledge in undertaking basic genetic association studies to unravel the genetic underpinnings of diverse phenotypes, either on human samples or other organisms.
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January 14, 2025 at 9:00 AM EST
Intrinsically disordered proteins (IDPs) are involved in a plethora of biological processes, yet their study requires specialized resources. This tutorial aims to introduce participants to three key resources in the field: DisProt (http://www.disprot.org), MobiDB (https://mobidb.org/) and the Protein Ensemble Database (PED) (http://proteinensemble.org). This session will provide insights into how these databases complement each other and their effective utilization in research. The tutorial will begin with an overview of the importance of studying IDPs, followed by general training on accessing and extracting data from DisProt, MobiDB, and PED. Participants will learn to navigate MobiDB to extract data and predictions of intrinsically disordered regions, and utilize PED to gain insights into the structural ensembles of IDPs. An exploration of how PED serves as a key resource for understanding the conformational diversity of IDPs will be included. A dedicated section will provide specialized biocuration training for DisProt, focusing on the curation process, structuring IDP-related data with ontologies, retrieving and defining IDP-related experiments, annotating states, transitions, and functions of IDPs, adhering to MIADE standards, and exploring thematic datasets and use cases in DisProt curation. By the end of the session, attendees will be equipped with the necessary knowledge to utilize key IDP resources and with the skills to contribute to the expansion of DisProt, providing them with IDP-specific biocuration skills and giving them the opportunity to expand the resource for the scientific community
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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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