Introduction

Genomics, the study of genomes, has become one of the most transformative fields in biological sciences. With the advent of high-throughput sequencing technologies, massive amounts of genomic data are being generated, necessitating advanced data science techniques to analyze, interpret, and make sense of this data. This course is designed to equip participants with the tools and techniques to effectively analyze genomic data, including handling large datasets, implementing machine learning algorithms, and applying statistical methods to extract meaningful insights.

Participants will gain hands-on experience in the computational techniques used in genomics and learn how to apply data science methods to real-world genomics problems such as gene expression analysis, variant calling, and genome-wide association studies (GWAS).

Objectives

By the end of this course, participants will:

• Understand the basics of genomics and sequencing technologies.
• Gain proficiency in handling and processing genomic data using tools like R and Python.
• Learn how to perform data quality control, normalization, and visualization on genomic datasets.
• Be familiar with statistical methods used in genomics, including differential gene expression analysis and GWAS.
• Apply machine learning techniques to genomics data for tasks like classification, clustering, and prediction.
• Be able to interpret and communicate results in the context of genomics research.

Who Should Attend?

This course is ideal for:

• Bioinformaticians and computational biologists who want to deepen their knowledge of data science techniques in genomics.
• Data scientists and analysts with a background in biology or healthcare who are looking to apply their data science skills in genomics.
• Researchers in genomics, systems biology, or personalized medicine who wish to enhance their computational capabilities.
• Students or professionals in genetics, bioinformatics, and computational biology who want to learn how to analyze genomic data using data science tools.

Day 1: Introduction to Genomics and Data Science Tools

Morning Session: Introduction to Genomics and High-Throughput Sequencing

• What is genomics? Overview of genomes, genes, and the importance of genomics in modern biology.
• Overview of sequencing technologies: Illumina, PacBio, Oxford Nanopore, and their applications.
• Introduction to genomic data formats: FASTA, FASTQ, VCF, GFF, BAM, and SAM.
• Key concepts: DNA sequencing, transcriptomics, variant calling, and functional genomics.
• Overview of common bioinformatics tools and pipelines (e.g., STAR, GATK, BWA).
• Introduction to R and Python for bioinformatics: Setting up the environment, packages, and libraries (e.g., Bioconductor, pyGenomeTracks, pandas).

Afternoon Session: Introduction to Data Processing and Quality Control

• Preprocessing genomic data: FastQC for quality assessment, trimming, and filtering raw sequencing data.
• Quality control metrics: Phred score, GC content, duplication rate, and alignment metrics.
• Basic data manipulation using Python (pandas) and R (dplyr): Importing and cleaning genomic data.
• Hands-on: Quality control of sequencing data using FastQC, trimming using Trimmomatic, and visualization of results.

Day 2: Exploring Genomic Data with R and Python

Morning Session: Data Visualization in Genomics

• Visualizing genomic data: Plotting read depth, GC content, and sequence quality.
• Using R and Python for genomics: Basic visualization techniques with ggplot2 (R) and matplotlib (Python).
• Heatmaps and clustering: Visualizing gene expression data and differential expression analysis.
• Introduction to genome-wide visualization: Plotting genome tracks using pyGenomeTracks and IGV.
• Hands-on: Visualizing gene expression data and sequencing quality metrics using R and Python.

Afternoon Session: Normalization and Differential Expression Analysis

• Gene expression data: Understanding RNA-Seq, counts, and TPM/FPKM/RPKM.
• Normalization techniques for RNA-Seq data: TMM, RPKM, FPKM, DESeq2, and edgeR methods.
• Differential gene expression analysis: Identifying upregulated and downregulated genes using DESeq2.
• Statistical methods for differential expression: p-values, fold changes, and multiple testing correction (FDR).
• Hands-on: Performing differential gene expression analysis on RNA-Seq data using DESeq2 (R).

Day 3: Variant Calling and Genomic Association Studies

Morning Session: Introduction to Variant Calling

• What is variant calling? Types of variants: SNPs, indels, and structural variants.
• Overview of variant calling pipelines: GATK, FreeBayes, and Samtools.
• Quality control of variant calls: Filtering based on depth, allele frequency, and quality scores.
• Interpreting VCF files: Alleles, genotypes, annotations, and genomic regions.
• Hands-on: Performing variant calling on sequencing data using GATK or FreeBayes and interpreting results.

Afternoon Session: Genome-Wide Association Studies (GWAS)

• Introduction to GWAS: Understanding the relationship between genetic variants and phenotypes.
• Preprocessing GWAS data: Quality control, imputation, and normalization of SNPs.
• Statistical tests for GWAS: Chi-square test, logistic regression, and linear regression models.
• Identifying significant SNPs and interpreting GWAS results: Manhattan plots and QQ plots.
• Hands-on: Performing a basic GWAS analysis on a dataset using PLINK, R, and visualizing results with ggplot2.

Day 4: Machine Learning in Genomics

Morning Session: Introduction to Machine Learning in Genomics

• Overview of machine learning techniques in genomics: Classification, regression, clustering, and dimensionality reduction.
• Feature selection and extraction: Identifying relevant features (e.g., gene expression levels, SNPs).
• Supervised learning methods: Logistic regression, decision trees, and random forests.
• Unsupervised learning methods: K-means clustering, hierarchical clustering, and PCA.
• Hands-on: Preprocessing genomic data for machine learning and applying classification algorithms in R or Python.

Afternoon Session: Deep Learning and Genomics

• Introduction to deep learning in genomics: Neural networks and convolutional neural networks (CNNs) for genomic data.
• Applications of deep learning in genomics: Predicting gene function, mutation effects, and disease associations.
• Using deep learning frameworks like TensorFlow and PyTorch for genomic data analysis.
• Hands-on: Building a simple neural network for classifying genomic sequences or predicting gene expression using deep learning tools.

Day 5: Advanced Genomic Applications and Final Project

Morning Session: Advanced Genomic Applications

• Metagenomics: Analyzing microbial communities and identifying species from sequencing data.
• Epigenomics: Analyzing DNA methylation, histone modification, and chromatin accessibility.
• Single-cell genomics: Understanding gene expression at the single-cell level.
• Population genomics: Analyzing genetic variation within populations and phylogenetic analysis.
• Hands-on: Applying genomic tools to metagenomic or epigenomic datasets and interpreting results.

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants work on a real-world genomics dataset (e.g., RNA-Seq, GWAS) to analyze and present findings.
• Presentation of results: Data visualization, statistical analysis, and machine learning models.
• Discussion of challenges and key takeaways from the course.
• Final Q&A and feedback session.
• Certification of completion for participants who successfully complete the course and final project.

Materials and Tools:

• Software and tools: R, RStudio, Python, Bioconductor, GATK, PLINK, FastQC, DESeq2, edgeR, matplotlib, ggplot2, TensorFlow, PyTorch
• Real-world genomics datasets: RNA-Seq, SNP, GWAS, metagenomics, epigenomics datasets
• Recommended readings: “Bioinformatics Data Skills” by Vince Buffalo, “Biostatistics for the Biological and Life Sciences” by Ron S. Kenett

Conclusion and Final Assessment

• Recap of key concepts: Data preprocessing, differential expression, variant calling, GWAS, machine learning, and deep learning in genomics.
• Final assessment: Presentation and evaluation of participants’ final projects.
• Certification of completion for participants who successfully complete the course and final project.