Genomic data is of growing importance for medicine, pharma, biotech and agriculture. Genomic medicine is already transforming our understanding of genetic diseases, cancer and other types of health-related conditions. In the pharmaceutical industry, genomic biomarkers and cancer driver mutations are considered regularly in diagnosis and drug prescription. We are seeing how genomic data analysis is changing the way chronic diseases are monitored for drug resistance (Tuberculosis, antibiotics), viral pathogens are analyzed to determine virulence and possibility of pandemic spread and patients screened for risk of cancer and other diseases. To understand this data, its use and learn how to analyze genomic data, we are inviting you to join us for a 3 month Online Genomics Training program.
OmicsLogic: Genomic Data Analysis is a 3-month genomics online training program that will provide an in-depth overview of bioinformatics concepts and analytical tools used to study genomic variation. Each week, participants will meet online to discuss various aspects of NGS data processing, genomic data analysis, and interpretation. After the first introductory lectures, participants will have the opportunity to get guidance and work on curated datasets or develop independent projects that leverage the tools and concepts covered in this program. We will meet twice a week, each Tuesday and Wednesday.
In this program, we will cover both theoretical and practical aspects of genomics:
Processing of Genomic data (part 1: Wednesday, July 24 – part 2: Tuesday, July 30)
○ What is DNA, Variation, and Significance
○ Next Generation Sequencing (WES, WGS, Targeted panels)
○ Read length/quality, Mapping NGS data to a reference Genome
What is a genome? What are the genome elements that make us unique? How can we decode DNA? During this session, you will understand the fundamentals of genomics and the technologies that enable the reading of the DNA code. You will learn to distinguish among the different DNA sequencing applications and to choose the more suitable one to answer your question. Moreover, you will get into action by learning the first steps of bioinformatics analysis of DNA sequencing data.
Understanding DNA Variation (part1: Wednesday, July 31 – part2: Tuesday, August 6)
○ Germline variation
○ Somatic mutations – reference and “normal” sample
○ Non-point variations (CNV, chromosomal instability, rearrangements)
The DNA sequence of any two individuals is 99.9% identical. The remaining 0.1% is the genomic variation that makes each and every one of us unique. During this session, we will discuss the different types of variation that we can detect in a genome of either an organism or a tumor, and how this difference can be quantified.
Analysis of Genomic Variants (part1: Wednesday, August 7 – part2: Tuesday, August 13)
○ Accurate detection – the significance of coverage distribution
○ Filtering – limiting the number of potentially significant variants
○ Statistical methods to evaluate variant significance, stratification
In this session, we will build on the topics we covered so far and our understanding of the different types of genomic variation. We will review the analytical methods used for accurate detection of variants and for the identification of significant ones – usually the ones that are contributing towards the development of a phenotype. We will touch on the statistical aspects of genomics and try to understand the algorithmic methods that are typically used.
Annotation and Interpretation (part1: Wednesday, August 14, – part2: Tuesday, August 20)
○ Types of variants missense, synonymous, nonsense
○ Prediction of variant effects on protein structure/function, P-P interaction, abnormal splicing
○ Using reference databases for variant annotation
○ Population genetics – phylogeny, 1000G project
What is the effect that a variant has on protein function? How can a particular mutation affect cell mechanisms and lead to the physical symptoms of a disease? How common is a variant in different populations? In this session, you will go through all the possible answers to these questions by exploring the public resources that provide us with such information.
Using Machine Learning for Genomic Data (part1: Wednesday, August 21, – part2: Tuesday, August 27, part3: Wednesday, August 28)
○ Regression, factors, and features – Factor Regression Analysis
○ Supervised Machine Learning (Classification, Discriminant Analysis)
○ Mutational Profiling, Signatures, and biomarkers
Machine learning is a rapidly growing domain that is primed to revolutionize many fields. In this session, you will learn how to explore genomic data with machine learning and mathematical modeling methods in order to gain relevant insights that will help you understand genome biology and its application in other fields, such as medicine and pharmacology.
Planning a genomics project (September 1-28)
○ Cancer studies (several examples)
○ Development of treatment resistance (Tuberculosis)
○ Case-control studies, GWAS
In the last part of the program, we will provide you with examples of how to analyze genomic data for different applications, such as cancer studies, development of treatment resistance and Genome Wide Association Studies. In the end, you will be able to design your own study and perform your own analyses of large scale genomics data.
To learn more and register, please visit: https://edu.t-bio.info/organizations/omicslogic-genomics-training-program/