Genomic Data Science

Introduction

Welcome to Genomic Data Science book modules! This book provides an overview of the principles and techniques associated with genomic data science.

Students will gain a comprehensive understanding of innovative technologies, major bioinformatics databases, and methods for analyzing and interpreting diverse genomic data. Readers will gain hands-on experience in application development and programming for next-generation sequencing (NGS) data analysis workflows.

Additionally, they will receive a comprehensive overview of modern techniques used in genomic data science including machine learning algorithms and big data analytics.

Additionally, readers will learn about open-source software tools to investigate omics structures such as genomics, transcriptomics, proteomics, metagenomics/meta transcriptomics.

By the end of this book readers will have acquired a basic working knowledge of how to analyze large datasets generated from next generation sequencing (NGS) technologies.

 

Objectives for Genomic Data Science

1. Understand the fundamentals of genomic data science, including its methods and applications.
2. Analyze large-scale genomic datasets to extract meaningful insights into biological systems.
3. Learn how to interpret and present complex genomics results in a clear manner that can be effectively communicated to various stakeholders.
4. Enhance understanding of genomics technologies such as next-generation sequencing, single-cell genomics, metagenomics, and others.
5. Develop proficiency with relevant programming languages (e.g., Python, R) and software tools necessary for analyzing genomic data efficiently and accurately.
6. Comprehend best practices in ethical issues related to genome data sharing and research activities involving human genetic material or other sensitive information from individuals or populations (ePrivacy).

 

Table of Contents

Module 1: Introduction to Genomic Data Science

-Overview of the genomic data science field

-Fundamentals of genomic data science

-Principles of genomic data science

-Methods of genomic data science

-Applications of genomic data science

–Technologies used in genomic data science

-Overview of biological processes in genomic data science

-Data Sources -How to collect and store data from various sources

-Overview of public databases and other online tools.

-Bioinformatics Basics

– Introduction to basic bioinformatic tools and techniques

-Storing and organizing data

-Data analysis & visualization.

 

Module 2: Modern Techniques used in genomic data science.

-Large-scale genomic datasets to extract meaningful insights into biological systems.

-Machine learning algorithms

-Big data analytics.

 

Module 3: Sequence Analysis

– Analyzing sequence data to identify mutations

-Single nucleotide polymorphisms (SNPs)

-Gene expression & regulation

-Copy number variation (CNV)

-Structure variation.

-Open-source software tools to investigate omics structures

-Proteomics & Metabolomics

Genomics and Transcriptomics

-Overview of proteomic and metabolomic techniques

-Using software for analyzing proteins & metabolites.

– Genomics technologies such as next-generation sequencing, single-cell genomics, and metagenomics.

-Interpret and present complex genomics results in a clear manner that can be effectively communicated to various stakeholders.

 

Module 4: Computer Programming for Genomics

-Relevant programming languages (e.g., Python, R)

-Software tools necessary for analyzing genomic data efficiently and accurately.

– Programming languages for genomics research like Python/R/Matlab etc.

-Machine learning algorithms to organize large datasets

-Text mining for predicting gene expression patterns.                              -Population Genetics & Evolutionary Biology

– Exploring population genetics principles in evolutionary biology towards medical applications.

 

Module 5: Clinical Applications

– Use of genomics in personalized medicine through precision diagnostics & therapeutics.

-Designing clinical trials utilizing advanced sequencing technology -Scientific report writing methods.

-Genome data sharing and research activities involving human genetic material

-Other sensitive information from individuals or populations (ePrivacy).

                             

Module 6: Questions and answers