In-person Tutorials (All times PET)

• Tutorial IP1: Hello Nextflow: Getting started with workflows for bioinformatics
• Tutorial IP2: From Cells to Clusters: An Introduction to Single-Cell RNA-seq Analysis in Python
• Tutorial IP3: AI-Powered Workflows for Modern Bioinformatics: Multiomics, Immunoinformatics, and Protein Structural Analysis

Satelite Tutorials (All times PET) - Held off-site from the main conference (Only available to Professional and Post-Doc registrants)

• Tutorial ST1: The Ensembl platform: Empowering data-focused research
• Tutorial ST2: Managing a bioinformatics core facility: Operating scientific services in a sustainable research infrastructure

Virtual Tutorials: (All times PET) - Presented through the conference virtual platform

• Tutorial VT1: Single-cell Data Analysis using R & Coexpression Networks generation and Analysis
• Tutorial VT2: Clustering and Classification of omic data using ML & Al tools (DNN & Transformers) applied to biodata

Room: TBD
Date: November 10, 2026
Start Time: 09:00
End Time: 17:00
Max Participants: 

Speakers

• Marcel Ribeiro-Dantas, PhD

Description

Nextflow is a powerful and flexible open-source workflow management system that simplifies the development, execution, and scalability of data-driven computational pipelines. It is widely used in bioinformatics and related scientific fields to automate complex analyses, making it easier to manage and reproduce large-scale data analysis workflows.

Hello Nextflow is intended as a “getting started” course for students and early-career researchers who are completely new to Nextflow. The tutorial aims to equip participants with foundational knowledge and skills in three key areas: (1) understanding the logic of how data analysis workflows are constructed, (2) Nextflow language proficiency and (3) command-line interface (CLI) execution.

Through guided, goal-oriented exercises, participants will learn to:

• Use core components of the Nextflow language to construct simple multi-step workflows effectively.
• Launch Nextflow workflows locally, navigate output directories to access results, interpret log outputs for insights into workflow execution, and troubleshoot basic issues that may arise during workflow execution.

By the end of the tutorial, participants will be well-prepared for tackling the next steps in their journey to develop and apply reproducible workflows for their scientific computing needs.

Additional study-at-home materials will be provided for them to continue learning and developing their skills further.

The training materials are open-source and freely available on the Nextflow training portal at https://training.nextflow.io/latest/hello_nextflow

Learning Objectives

This tutorial aims to teach foundational skills for building and running pipelines with Nextflow:

• Launch a Nextflow workflow locally
• Find and interpret outputs (results) and log files generated by Nextflow
• Troubleshoot common issues
• Utilize core Nextflow components sufficient to build a simple multi-step workflow
• Describe next-step concepts such as channels and operators
• Configure pipeline execution to run on a variety of common computing platforms including HPC and cloud
• Apply best practices for reproducibility, portability and code re-use that make pipelines FAIR, including code modularity and software containers

Intended Audience and Level

This tutorial is designed for learners who are completely new to Nextflow. Some familiarity with the command line, basic scripting concepts and common file formats is assumed. The exercises are all domain-agnostic, so no prior scientific knowledge is required.

Schedule

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Room: TBD
Date: November 10, 2026
Start Time: 09:00
End Time: 13:00
Max Participants: 40

Speakers

• Gerda Cristal Villalba Silva, PhD

Description

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to study cellular diversity, gene expression dynamics, and disease mechanisms at single-cell resolution. Despite its growing importance across biomedicine, ecology, and translational research, the computational skills required to process and interpret scRNA-seq data remain a barrier for many researchers — particularly in Latin America, where access to institutional computing resources and advanced bioinformatics training is unequal.
This half-day, fully hands-on tutorial provides a beginner-friendly introduction to scRNA-seq data analysis using Python and the Scanpy ecosystem, running entirely on Google Colab — no local installation required. Participants will work through a complete, reproducible pipeline: from loading a raw count matrix to performing quality control, dimensionality reduction, clustering, and cell type annotation of a public dataset. By the end of the session, attendees will be equipped to independently begin analyzing their own single-cell datasets.

Learning Objectives

By the end of this tutorial, participants will be able to:

1. Explain the principles of scRNA-seq and describe the structure of single-cell count data (AnnData / h5ad format).
2. Perform quality control filtering based on mitochondrial content, gene counts, and UMI thresholds using Scanpy.
3. Normalize, log-transform, and select highly variable genes for downstream analysis.
4. Run PCA and generate UMAP embeddings for dimensionality reduction and visualization.
5. Apply Leiden graph-based clustering to identify distinct cell populations.
6. Identify marker genes per cluster using rank_genes_groups and perform basic manual cell type annotation.
7. Locate and use public resources for continued learning and dataset exploration.

Intended Audience and Level

Level: Beginner
No prior scRNA-seq or single-cell bioinformatics experience is required.

Target audience:

• Graduate students and early-career researchers in biology, biomedical sciences, and related disciplines across Latin America.
• Wet-lab scientists who generate or plan to generate scRNA-seq data and want to analyze it independently.
• Bioinformaticians from adjacent domains (genomics, metagenomics, proteomics) transitioning to single-cell methods.
• Researchers for whom Google Colab provides a critical low-barrier, cloud-based entry point (no HPC required).

Prerequisites:

• Basic Python familiarity (variables, lists, functions) — or willingness to follow along with pre-written code scaffolds.
• A Google account (for Colab access). No software installation required.

Schedule

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Room: TBD
Date: November 10, 2026
Start Time: 13:00
End Time: 17:00
Max Participants: 40

Speakers

• David Requena, BSc, MPH, PhD
• Cleidy Osorio Mogollón, BSc, MSc, PhD
• Juan Andrade Martinez, BSc, MPH, PhD(c)
• David Tachiquin, BE, MSc
• Daniel Garbozo, BSc
• Jennifer Almeyda, BSc

Description

The cost of high-throughput sequencing has dropped dramatically, by roughly 15-fold over the past decade. This has driven
the widespread adoption of sequencing technologies and enabled the generation of large-scale omics datasets. AI-driven
tools have been trained on these datasets and specialized for different data modalities, learning complex biological
patterns and extrapolating predictions to new samples. These approaches now accelerate processing, biological
interpretation and experimental planning, making AI-powered workflows increasingly essential in modern bioinformatics
practice.

This half-day tutorial will combine conceptual introductions with hands-on exercises designed for early- and mid-career
scientists. The main goal is to introduce bioinformaticians and biomedical researchers to practical AI-driven workflows
and facilitate their adoption in contemporary biomedical research. Participants will be guided through reproducible
exercises in Google Colab covering three major areas of applied bioinformatics: genomics, immunoinformatics, and
protein structural biology.

The tutorial is organized into four modules: 1) the introductory module: essential concepts in artificial intelligence and
machine learning, including model types, structure, training techniques, and foundation models; 2) the omics module:
theory and hands-on exercise using AlphaGenome, guiding participants from genome sequence data to the prediction of
regulatory activity, gene expression, chromatin architecture, and prioritization of non-coding variants; 3) the
immunoinformatics module: theory and an exercise going from proteome data, covering epitope prediction using
machine-learning tools and the application of filters for epitope prioritization; and 4) the structural biology module: thery
and a practical case study on protein structure prediction, validation, visualization, and interpretation using AI-powered
tools such as AlphaFold, ColabFold, and related software.

These case studies reflect common and rapidly growing applications of bioinformatics in biomedical research and are
particularly relevant to scientific communities in Latin America. The tutorial will be delivered by active researchers with
scientific publications in these fields, affiliated with BigMind (the Bioinformatics Group in Multiomics and Immunology, a
research training initiative with members of different countries from America), RIABIApro (CYTED Iberoamerican Network
on AI for Biosciences and Biotechnology), New York University, Weill Cornell University and the University of Sao Paulo.

Learning Objectives

By the end of this tutorial, participants will be able to:

• Understand the basic principles and limitations of AI tools in modern bioinformatics.
• Build and implement AI-powered bioinformatics workflows.
• Execute AI-driven analyses and predict biological outcomes.
• Critically interpret AI-derived results, outputs, and metrics.
• Test and validate AI-derived insights using complementary computational approaches.

Intended Audience and Level

• Target Audience: Intermediate-level researchers, graduate students, and bioinformaticians who handle biological data (Genomics, Immunology, or Structural Biology) and wish to transition from classical analysis to AI-assisted workflows.
• Experience Level: Intermediate. Participants should have a basic understanding of molecular biology and experience with basic bioinformatics (e.g., handling FASTA/FASTQ files or basic command-line usage).
• Prerequisites: Familiarity with NGS data analysis (e.g., RNA-seq, ChIP-seq, ATAC-seq, or Hi-C) and with HLA/epitope analysis workflows. Basic Python or R literacy.
• Mandatory: An active Google account to access Google Colab notebooks.
• Desirable: Basic familiarity with Bash command-line usage.
• Not Required: Prior knowledge of deep learning, transformer architectures, or sequence foundation models. No local HPC infrastructure is needed.

Target audience profile
Intermediate-level researchers, graduate students, and bioinformaticians in the life sciences who are looking to transition
from classical analysis to AI-assisted workflows. This tutorial is designed for those who handle biological data (Genomics, Immunology, or Structural Biology) and want to leverage Foundation Models and Deep Learning tools to accelerate their
research.

Schedule

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Venue: International Potato Center (CIP) Headquarters, Lima, Peru - Auditorium
Date: November 9, 2026
Start Time: 09:00
End Time: 16:00
Max Participants: 30

Speakers

• Dr. Aleena Mushtaq Stolworthy, PhD
  Senior Ensembl Outreach Officer
  European Bioinformatics Institute (EMBL-EBI)
  Wellcome Genome Campus, Hinxton, United Kingdom

Description

The Ensembl platform is one of the world’s leading open-access resources for genome-centric data integration, exploration, and interpretation. Developed at the European Bioinformatics Institute (EMBL-EBI) Ensembl provides researchers with comprehensive access to genome assemblies, gene annotations, comparative genomics resources, genetic variation datasets, and regulatory information across a broad range of species.

This full-day tutorial will introduce participants to the Ensembl ecosystem and demonstrate how Ensembl resources can support data-driven biological and genomic research. The workshop will combine conceptual presentations with practical demonstrations and guided exercises focused on genome browsing, annotation interpretation, variant analysis, and scalable data access methods.

Participants will explore how Ensembl integrates multiple biological datasets, learn how to navigate and visualize genomic information through the Ensembl Browser, and gain experience using Ensembl tools for research workflows. The tutorial will also highlight Ensembl’s training programme and capacity-building initiatives designed to support researchers, educators, and institutions worldwide.

Through interactive case studies and a collaborative data exploration project, attendees will develop practical skills for applying Ensembl resources to real-world genomics questions in research.

Learning Objectives

By the end of this tutorial, participants will be able to:

• Navigate the new Ensembl interfaces to identify available genomic data
• Examine genome assembly details to select assemblies suitable for your work
• Explore genes, variants and regulatory annotations and their sources
• Use Ensembl tools such as Ensembl Variant Effect Predictor (VEP) to determine variant consequences
• Design a three-step approach to explore a feature for further research using Ensembl data

Intended Audience and Level

This tutorial is designed for researchers, educators, bioinformaticians, data scientists, laboratory scientists, and professionals working in genomics, molecular biology, biodiversity, agriculture, health sciences, and related disciplines.

The workshop is suitable for participants with diverse levels of experience, including:

• Beginners seeking an introduction to genome resources and genomic data analysis;
• Participants with prior experience in genomics or bioinformatics who wish to strengthen their practical skills using Ensembl tools and datasets;
• Advanced users interested in scalable access methods, annotation workflows, and integrative genomic analyses.

No advanced programming experience is required, although familiarity with basic biological
concepts and genomic terminology will be beneficial.

Schedule

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Venue: 
Date: November 9, 2026
Start Time: 09:00
End Time: 16:00
Max Participants: 45

Speakers

• ThankGod Ebenezer, EMBL-EBI - Global Engagement Program, UK | Global Engagement Manager
• Patricia Carvajal-López, EMBL-EBI - Global Engagement Program, UK | Senior Scientific Project Manager
• Luis Tataje Lavanda, Instituto de Investigación en Salud Global - ISG, Universidad Privada San Juan Bautista, Lima, Peru | Principal Investigator
• Pablo Tsukayama, Universidad Peruana Cayetano Heredia, Peru | Associate Professor
• Cleidy Osorio, University of Sao Paulo, Brazil/Peru | Doctoral Researcher
• David Requena, New York University & New York Genome Center, USA/Peru | Principal Investigator
• Nicole Scherer, Instituto Nacional de Câncer - INCA, Brazil | Core Facility Manager
• Yalbi Itzel Balderas-Martínez, Instituto Nacional de Enfermedades Respiratorias Ismael Cosio Villegas - INER | Principal Investigator
• Vinicius Maracaja-Coutinho, Universidad de Chile | Assistant Professor

Description

This knowledge exchange workshop is an opportunity for members or leads of bioinformatics core facilities or services to learn from each other and EMBL-EBI’s service teams. This is also an opportunity for institutional leaders interested in establishing, managing, or strengthening bioinformatics core facilities and services in Latin America.

These resources play an essential role in enabling research in the life sciences. The landscape is constantly evolving as new research tools emerge, as experiments become increasingly data-intensive and often entirely in silico, and as their users – experimental researchers – become more exposed to the power of data-driven biology.

This course will allow participants to share experiences, discuss challenges and solutions that they face, and plan ways to cope with the ever-changing demands raised by the molecular life science field. Participants will gain insights into how successful bioinformatics core facilities operate, how to balance service and research activities, how to manage collaborations and users, and how to build sustainable scientific infrastructure in resource-variable environments. Although the management principles covered in the programme are universal, this event takes the opportunity to delve into challenges and opportunities within Latin America.

Learning Objectives

By the end of this tutorial, participants will be able to:

• Identify structure and operational models of bioinformatics core facilities and services.
• Identify communication strategies within interdisciplinary teams and scientific collaborations.
• Define best practices for core user management and user training. 1
• Identify opportunities for shared

Intended Audience and Level

This workshop is aimed at both new and experienced managers of bioinformatics core facilities, or other facilities that support their users to analyse and interpret large biomolecular data sets. Audience may include, but is not limited to:

• Bioinformaticians and computational biologists in charge of analysis or service provision in their teams
• Core facility managers
• Principal investigators
• Postdoctoral researchers
• Institutional administrators
• Researchers are planning to establish core facilities or services

Schedule

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Date: November 9, 2026
Start Time: 16:00
End Time: 20:00
Max Participants: 25

Speakers

• Dr. José Molina-Mora, Faculty of Microbiology and CIET, University of Costa Rica (UCR), Costa Rica
• Dr. Flavio E. Spetale, CIFASIS (CONICET/UNR), Rosario, Argentina
• Dr. Javier De Las Rivas, Cancer Research Center (CiC) CSIC & University of Salamanca (USAL), Spain
• Dr. Deisy Morselli Gysi , Federal University of Paraná (UFPR), Brazil

Description

The exponential growth of biological data, from omics experiments to single-cell measurements, demands computational approaches that combine classical bioinformatics with modern Artificial Intelligence (AI). This Micro-Course offers a comprehensive, hands-on introduction to four complementary sessions presenting presenting methodologies that are applied to the analysis of biological data: (1) Clustering and Classification of omic data using Machine Learning, (2) AI and Transformer-based models for biodata, (3) Deep analysis of single-cell omic data, and (4) Construction and analysis of co-expression biological networks. Across four guided sessions, participants will work with real datasets to use reproducible and well-documented workflows in R and Python, moving from data preprocessing to model interpretation and biological insight. The Micro-Course is jointly organized by members of RIABIApro (CYTED Network on AI in Bioinformatics) and BiotrAIn (Fundamental and sustainable curriculum on artificial intelligence for bioscientists from Latin America, by EMBL-EBI, the University of Costa Rica and CABANAnet), bringing together experienced trainers from Latin American and European institutions. Each session is self-contained but designed to build a coherent picture of how AI and bioinformatics integrate to extract knowledge from heterogeneous biological data.

Learning Objectives

By the end of this tutorial, participants will be able to:

• Apply Machine Learning techniques (clustering and classification) to high-dimensional omic datasets, including feature selection, model evaluation and interpretation.
• Understand the foundations of modern AI architectures, particularly Transformers, and their application to biological sequences and biomedical data.
• Perform end-to-end analysis of single-cell transcriptomic data: quality control, normalization, dimensionality reduction, clustering, cell-type annotation and differential expression.
• Build and analyze co-expression biological networks, including topological characterization, module detection and biological interpretation.

Intended Audience and Level

Target audience: graduate students (MSc, PhD), postdoctoral researchers, and early-career scientists working in bioinformatics, computational biology, biomedical sciences, agricultural or microbial sciences, who wish to incorporate AI and modern bioinformatic methods into their research.

Level: intermediate. Participants are expected to have basic prior experience with R or Python (reading and running scripts, installing packages) and a general understanding of molecular biology and omics data. No prior expertise in Machine Learning or Deep Learning is required: foundational concepts will be introduced before each hands-on block. Advanced learners will benefit from the Transformer and single-cell sessions, which include recent state-of-the-art approaches.

Schedule
Coming Soon

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Date: November 10, 2026
Start Time: 16:00
End Time: 20:00
Max Participants: 25

Speakers

• Dr. José Molina-Mora, Faculty of Microbiology and CIET, University of Costa Rica (UCR), Costa Rica
• Dr. Flavio E. Spetale, CIFASIS (CONICET/UNR), Rosario, Argentina
• Dr. Javier De Las Rivas, Cancer Research Center (CiC) CSIC & University of Salamanca (USAL), Spain
• Dr. Deisy Morselli Gysi , Federal University of Paraná (UFPR), Brazil

Description

The exponential growth of biological data, from omics experiments to single-cell measurements, demands computational approaches that combine classical bioinformatics with modern Artificial Intelligence (AI). This Micro-Course offers a comprehensive, hands-on introduction to four complementary sessions presenting presenting methodologies that are applied to the analysis of biological data: (1) Clustering and Classification of omic data using Machine Learning, (2) AI and Transformer-based models for biodata, (3) Deep analysis of single-cell omic data, and (4) Construction and analysis of co-expression biological networks. Across four guided sessions, participants will work with real datasets to use reproducible and well-documented workflows in R and Python, moving from data preprocessing to model interpretation and biological insight. The Micro-Course is jointly organized by members of RIABIApro (CYTED Network on AI in Bioinformatics) and BiotrAIn (Fundamental and sustainable curriculum on artificial intelligence for bioscientists from Latin America, by EMBL-EBI, the University of Costa Rica and CABANAnet), bringing together experienced trainers from Latin American and European institutions. Each session is self-contained but designed to build a coherent picture of how AI and bioinformatics integrate to extract knowledge from heterogeneous biological data.

Learning Objectives

By the end of this tutorial, participants will be able to:

• Apply Machine Learning techniques (clustering and classification) to high-dimensional omic datasets, including feature selection, model evaluation and interpretation.
• Understand the foundations of modern AI architectures, particularly Transformers, and their application to biological sequences and biomedical data.
• Perform end-to-end analysis of single-cell transcriptomic data: quality control, normalization, dimensionality reduction, clustering, cell-type annotation and differential expression.
• Build and analyze co-expression biological networks, including topological characterization, module detection and biological interpretation.

Intended Audience and Level

Target audience: graduate students (MSc, PhD), postdoctoral researchers, and early-career scientists working in bioinformatics, computational biology, biomedical sciences, agricultural or microbial sciences, who wish to incorporate AI and modern bioinformatic methods into their research.

Level: intermediate. Participants are expected to have basic prior experience with R or Python (reading and running scripts, installing packages) and a general understanding of molecular biology and omics data. No prior expertise in Machine Learning or Deep Learning is required: foundational concepts will be introduced before each hands-on block. Advanced learners will benefit from the Transformer and single-cell sessions, which include recent state-of-the-art approaches.

Schedule
Coming Soon

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