Public outreach in Taiwan’s ecology. Join us!

Free Gift: Exclusive biodiversity ceramic coaster for all attendees.
Special Offer: 22% off on book purchases.
Bonus: Book signing session and Environment Education hours available.

Course Description

This course introduces the workflow of bulk RNA-seq, including experimental design, quality control (QC), normalization, differential expression analysis (DEG), and pathway analysis. Participants will learn how to interpret transcriptomic data and translate results into biological insights.

Course Outline

1. Fundamentals and Experimental Workflow of Bulk RNA-seq

2. Data Preprocessing and Quality Control (QC)

3. Normalization and Data Exploration

4. Differential Expression Analysis (DEG)

5. Pathway Analysis and Biological Interpretation

【Class Requirements】If you do not have an account yet, please register first.

Course Description
This course will provide a brief introduction to QIAGEN Ingenuity Pathway Analysis (IPA) and its recent updates, using a published multi-omics study on adverse cardiac remodeling and metabolic inflexibility as a case example. Participants will be guided through the practical workflow of metabolomics and phosphoproteomics data preparation, upload, analysis, and interpretation. The course will focus on canonical pathways, upstream regulators, networks, Comparison Analysis, Analysis Match, and IPA Interpret, helping participants understand how multi-omics data can be applied to biological mechanism exploration.

Course Outline
Part 1. Introduction to the IPA Platform and Recent Updates
Part 2. Literature Case Study and Research Question
Part 3. Data Preparation and Upload
Part 4. Hands-on Analysis of Phosphoproteomics Data
Part 5. Hands-on Analysis of Metabolomics Data
Part 6. Applications of Comparison Analysis and Analysis Match
Part 7. Introduction to IPA Interpret
Part 8. Multi-omics Integration and Biological Interpretation
Part 9. Discussion and Q&A

Object

Bridging the gap in experimental techniques through visualization. This session demonstrates how JoVE transforms complex protocols into visual guides to enhance research reproducibility and training efficiency.

Agenda

02 mins | Opening: Challenges in Biomedical Tech Standardization

03 mins | JoVE Overview: World-leading science video journal (Neuroscience, Immunology, Cancer, etc.)

12 mins | Specialized Applications for IBMS Research:

• Neuroscience: Electrophysiology and animal models.
• Cardiovascular & Metabolism: Protein stability and signaling pathways.
• Infection & Cancer: Vaccine development and DNA repair.
• Smart Data: Multiomics and translational medicine guides.

03 mins | Education & Talent: Supporting TIGP and lab-specific video playlists.

03 mins | Live Demo: Integrating JoVE into Google Classroom/My Class.

07 mins | Q&A: Building a dedicated database for IBMS.

Core Values

Higher Reproducibility: Minimize human error in experiments.

Faster Training: Shorten the learning curve for RAs and students.

Interdisciplinary Learning: Ease the transition into bioinformatics and advanced imaging.

Note: Light refreshments will be provided. All colleagues from the Division of Life Sciences are welcome to attend!

Day 1: Basic introduction and data processing workflow

NetMedEx is a Docker-based tool that integrates biological concepts (genes, chemicals, diseases) scattered across the literature by constructing interactive networks from over 30 million PubMed articles using PubTator3.

A key innovation is its Hybrid RAG-powered Chat Panel, which combines semantic retrieval (vector search of abstracts) with graph-based topological analysis (e.g., shortest paths, community detection). This enables dynamic, dialogue-based exploration of biological relationships grounded in both literature evidence and network structure.

In a case study on osteoporosis, NetMedEx identified potential mechanisms of herbal drugs via miRNA regulation, revealing interactions among compounds, miRNAs, and osteogenic pathways, and supporting hypothesis generation. Please visit https://github.com/lsbnb/NetMedEx for more details.

Important Note: Pre-session Preparation

To maximize the platform's potential during the session, please visit the NetMedEx website in advance. We highly recommend applying for your own API Key (OpenAI preferred) to set up the AI engine before the class.

Reference Link: https://github.com/cylin2022/NetMedEx#setup-ai-engine

Day 2: Advanced analysis, metabolite database, and application cases

This course is designed to empower participants with the skills to leverage Generative AI in academic contexts, significantly enhancing writing efficiency. The curriculum focuses on integrating tools like Gemini and NotebookLM to streamline literature analysis, synthesize reviews, and refine data interpretation—all while maintaining the highest standards of academic integrity. By upgrading both mindset and technical proficiency, participants will understand the boundaries of AI and transform it into a powerful asset for scholarly production.

Key Highlights:

• Writing Advancement: Understanding the functions, opportunities, and limitations of AI-assisted writing.

• Practical Toolset: Mastery of collaborative workflows using Gemini and NotebookLM.

• Academic Ethics: Navigating AI usage guidelines and ethical considerations for journal submissions.

Requirement: Please bring your laptop for the hands-on session.

This workshop shall teach attendees how to utilize the metabolomics resources available at the Taiwan Biobank. The first part of the workshop will serve as a brief introduction to the metabolomics field with an emphasis on what resources are available from the Taiwan Biobank. The second part is a hands-on session where attendees shall learn how to use the R statistical language and the MetaboAnalystR package to conduct metabolomics analysis on Taiwan Biobank data.

Agenda:

• What is metabolomics?
• Metabolomics applications.
• Metabolomics resources @ Taiwan Biobank .
• (Hands-on) Data preprocessing: Data cleaning, how to deal with missing values, and data normalization.
• (Hands-on) Statistical analysis: Exploring latent structures and sample correlations from the data using univariate and multivariate analyses.

Discover how EndNote 2025 helps you manage references with AI, making key-point extraction and summarization easier.

Learn EndNote’s core features and use CWYW to handle citations, references, and formatting all in one place while writing.

EndNote accelerates our research process so we can focus on what truly matters – conducting and sharing groundbreaking research. With the help of EndNote, we can collect, organize, manage, and even format references for our research efficiently. Let's make the most of EndNote.

Outline

•Install EndNote and create a library

•Collect References

•Manage and Groups

•Cite While You Write

•Back up and Share

Common annotation tools and their statistical idea