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David Paleček contact
Minimal examples on how to generate a knowledge graph from set of RO-Crates, and use it as a SPARQL endpoint either directly or using python rdflib. The examples are constructed from EMO-BON long-term omics observatory for marine biodiversity.
- Tutorial on Tess.elixir
- 🗓 Date: November 17, 10:30 - 12:30
- 📍 Location: Faculty of Pharmacy of the University of Porto.
Coverage: fuseki server, SPARQ endpoint and queries, EMO-BON Data Analysis Kit
Requirements: Jupyter Notebook, basics of python
Andrzej Tkacz contact
- Tutorial on Tess.elixir
- 🗓 Date: April 16, 15:00 - 17:00 (help with technical setup at 14:30)
- 📍 Location: CCMAR, Gambelas, building 7, room 1.39.
Large files or datasets—especially those containing genomic data—no problem. This workshop introduces essential Linux commands and simple Bash scripts to streamline data manipulation tasks. We’ll cover key operations such as searching for patterns, globally modifying content, and aligning DNA sequences, such as FASTAQ and FASTA files.
Coverage: bash, blast
Requirements: bash command line tool such, for windows WSL, or VBox are good. Alternatively, if comfortable, ask for account on HPC (contact David).
Tânia Aires contact, David Paleček contact
- Tutorial on Tess.elixir
- 🗓 Date: May 7, 15:00 - 17:00 (help with technical setup at 14:30)
- 📍 Location: CCMAR, Gambelas, building 7, room 1.39.
This workshop will provide an overview of microbiome data analysis from Illumina amplicon sequencing raw data to data processing, visualization and statistics. Participants will learn basic concepts and tools to preprocess (QIIME2) and analyse (MicrobiomeAnalyst) microbiome data, in particular bacteria associated with cold-water corals.
Coverage: QIIME2, MicrobiomeAnalyst
Requirements: HPC account (contact David) for running QIIME2
Isabel Duarte contact
- Tutorial on Tess.elixir
- 🗓 Date: June 4, 15:00 - 17:00 (help with technical setup at 14:30)
- 📍 Location: CCMAR, Gambelas, building 7, room 1.39.
Introduction to RNA-seq data analysis using R, focusing on differential expression analysis and learning how to choose the right Generalized linear model (GLM) for your question and your data. Participants will learn essential concepts and basic workflow, from preprocessing raw count data to identifying differentially expressed genes.
Coverage: BioConductor, DESeq2, tidyverse
Requirements: R studio
This project uses Quarto's _freeze/ caching. The _freeze/ folder is committed to the repo and marked as binary in .gitattributes to avoid merge conflicts. If in doubt, you can safely delete it and re-render with quarto render biohap/.