Gateway to Bioinformatics Knowledge Exchange

Instructor: Suryadip Sarkar, Guest Tutor at NyBerMan Bioinformatics India

To introduce participants to the end-to-end process of discovering disease biomarkers using RNA-seq data and machine learning, by demonstrating how gene expression differences can be translated into predictive models.

Webinar Highlights

• RNA-seq experimental overview
• Raw reads to gene counts: key steps (in brief)
• What is Differential Gene Expression Analysis (DGEA)
• Why is DGEA critical for biomarker discovery?
• Load count data (prepared dataset)
• Run DESeq2: normalisation, dispersion, DEG detection
• Visualise results: PCA, volcano plots, heatmaps
• Export the top differentially expressed genes
• Introduction to supervised learning
• Classification problem: disease vs. control
• Common algorithms (Logistic Regression, Random Forest)
• Data preparation for ML (features = DEGs, labels = phenotype)
• Load DEGs into a Python ML pipeline
• Train/test split, train a classifier (e.g., Logistic Regression)
• Evaluate using a confusion matrix, accuracy
• Identify important genes using feature importance

Learning Outcomes

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

1. Understand the purpose of RNA-seq in studying gene expression and identifying disease-relevant genes.
2. Explain the concept of differential gene expression and how tools like DESeq2 are used to detect it.
3. Interpret basic RNA-seq visual outputs, such as PCA plots and volcano plots.
4. Understand how DEGs can serve as features for ML models in a classification context.
5. Recognise the structure of a simple ML workflow, including data input, model training, and evaluation.
6. Appreciate the potential of combining RNA-seq with ML for biomarker discovery and be motivated to explore these techniques in depth through the full course.

Register Now to elevate your skills and stay ahead in the Applications of AI/ML in Genomics.

Instructor: Arwa Mo’men Elhabbaa, Guest Tutor at NyBerMan Bioinformatics

This webinar guides users through the analysis of RNA-seq gene expression data using the limma-voom workflow in Galaxy. Starting from a count matrix, users will learn how to normalize data, assess quality, perform differential expression analysis, and visualize results. The data used is from a real mouse study comparing gene expression in mammary gland cells across different physiological states.

Webinar Highlights

• Import and organize RNA-seq count data and associated sample metadata
• Perform data quality control and normalization
• Apply limma-voom to detect differentially expressed genes
• Use Glimma to generate interactive visualizations of results
• Interpret output tables and plots to draw biological insights

Learning Outcomes

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

1. Confidently use the Galaxy interface for RNA-seq analysis workflows
2. Apply statistical models for gene-level analysis of RNA-seq count data
3. Recognize and handle technical variation and sample bias in RNA-seq datasets
4. Identify and visualize differentially expressed genes
5. Prepare data for downstream analyses such as pathway enrichment or clustering

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Instructor: Dr. Saranyadevi, Guest Tutor at NyBerMan Bioinformatics

In this webinar, we’ll explore the role of docking-based virtual screening in modern drug discovery. With the increasing demand for cost-effective and time-saving approaches in identifying bioactive compounds, virtual screening, particularly docking-based methods, has become indispensable.

Webinar Highlights

• Introduce virtual screening and its role in drug discovery.
• Explain the principles and workflow of molecular docking using PyRx.
• Familiarize participants with key features of docking-based virtual screening in PyRx.
• Demonstrate compound library screening against a biological target.
• Discuss the advantages, limitations, and validation of docking-based methods.

Learning Outcomes

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

1. Participants will understand the basic features, system requirements, and applications of PyRx in molecular docking. They will also become familiar with the interface and tools of BIOVIA Discovery Studio Visualizer for molecular visualization and analysis.
2. Building on this foundation, they will learn to prepare protein structures by removing water molecules, adding hydrogens, and converting files into docking-compatible formats.
3. Next, they will prepare ligands through energy minimization, protonation, and format conversion (e.g., to .pdbqt), followed by molecular docking using PyRx—setting up the grid box and defining parameters.
4. Finally, they will learn how to analyze docking results, including binding affinity scores and ligand-binding poses.

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Instructor: Dr. Ghazala Sultan, Tutor at NyBerMan Bioinformatics

In today’s rapidly evolving cheminformatics landscape, mastering tools like RDKit is essential. This webinar is designed to provide a comprehensive foundation in cheminformatics fundamentals, an introduction to RDKit, and practical applications for effective chemical data analysis. Ideal for beginners and professionals looking to enhance their expertise.

Webinar Highlights

• Introduction to Python for cheminformatics
• Hands-on practice with basic data manipulation code
• Fundamentals of cheminformatics
• Overview of cheminformatics databases and data types
• Data representation techniques in cheminformatics
• Common tools in cheminformatics (with a focus on RDKit)
• Calculating molecular descriptors using RDKit (e.g., molecular weight, LogP, TPSA)
• Estimating molecular fingerprints using RDKit (e.g., Morgan and topological fingerprints)
• Machine learning model building and evaluation (e.g., Decision Tree)

Learning Outcomes

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

1. Understand the basics of cheminformatics and its applications.
2. Work with cheminformatics databases and represent chemical data effectively.
3. Utilize RDKit for molecular descriptor and fingerprint calculations.
4. Implement basic Python scripts for data manipulation.
5. Build and evaluate machine learning models for cheminformatics research.
6. Apply cheminformatics tools to streamline research and analysis workflows.

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Instructor: Ashish Kumar Choudhary (Scientist, IIT Bombay Alumni)

In today’s life sciences field, understanding Next-Generation Sequencing (NGS) and bioinformatics is essential. This webinar offers a foundational overview of these topics, equipping participants with the skills to manage and analyze large-scale biological data. It’s ideal for newcomers and those seeking to refine their expertise.

Webinar Highlights:

• NGS Overview: Basics of NGS and its modern applications.
• Data Formats: Learn various data formats crucial for bioinformatics.
• Bioinformatics Tools: Hands-on insights into STAR, BWA, HISAT2, QIIME, samtools, DAVID, and Pathway Tools.
• Databases: Explore NCBI, Ensembl, Single Cell Portal, Cell Marker, and GEO for efficient data retrieval.

Learning Outcomes:

• Understand NGS and bioinformatics databases.
• Gain practical knowledge of bioinformatics tools for data analysis.
• Interpret complex datasets for research applications.
• Prepare for future advances in genomics and bioinformatics.

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Instructor: Dr. Ghazhala Sultan, Guest Tutor at NyBerMan

Machine learning plays a crucial role in identifying hidden patterns. When integrated with gene expression analysis, it enables the prediction of biological outcomes, accelerates biomarker discovery, and tackles complex biological systems. This webinar is essential for researchers and scientists looking to deepen their understanding and applications of these areas, particularly in the context of differential gene expression analysis. By combining the power of machine learning with gene expression insights, participants will be equipped to make significant contributions to their fields.

Webinar Highlights:

• Overview of Machine Learning
• Understanding ML algorithms and their applications
• Machine Learning-Integrated Gene Expression Analysis (Microarray (CEL files), limma, Functional Enrichment analysis, Pathway analysis, and Network Analysis)

Learning Outcomes:

• Basic concepts of machine learning principles and their applications
• Overview of machine learning algorithms commonly used in gene expression analysis
• Master the integration of machine learning techniques with gene expression data, including data preprocessing, feature selection, and model building
• Gain hands-on experience in analyzing gene expression data using popular machine learning tools and libraries
• Develop the ability to interpret machine learning models and extract meaningful biological insights from gene expression data
• Proficiency in using tools like limma, EnrichR, and pathway analysis for biological interpretation of gene expression data
• Develop skills in network analysis to visualize and understand interactions between genes and biological pathways
• Apply machine learning-integrated gene expression analysis to your research projects to address complex biological questions and make novel discoveries

Register Now to elevate your research skills and stay ahead in the world of Machine Learning and Gene Expression Analysis.