Certificate in High Performance Computing for Genomics

This course is designed for bioinformaticians, computational biologists, and researchers in genomics who seek to enhance their skills in high-performance computing (HPC). Participants will gain expertise in using advanced HPC resources to process and analyze large genomic datasets efficiently, enabling faster and more accurate research.

Students will learn to write optimized code for parallel computing, utilize cloud-based HPC services, and apply best practices for managing and analyzing next-generation sequencing data. Practical hands-on sessions will ensure learners can implement these skills in real-world genomics projects.

1. 1. Introduction to High Performance Computing (HPC): Learners will explore the basics of HPC, including hardware architecture, operating systems, and programming models. They will gain foundational skills in managing HPC resources and writing basic parallel programs.
2. 2. Genomics Data Management: This module covers the storage, retrieval, and management of large genomics datasets. Learners will study file formats, data compression techniques, and database systems tailored for genomics data.
3. 3. Introduction to Genomics Workflow Management Systems: Learners will be introduced to workflow management systems such as Nextflow, CWL, and Snakemake. They will learn how to design and implement genomics workflows, manage tasks, and handle dependencies.
4. 4. High-Performance Sequence Alignment: This module focuses on efficient sequence alignment algorithms and their implementation using HPC. Learners will study algorithms like BWA, Bowtie, and Minimap2 and learn how to optimize their performance on HPC systems.
5. 5. Genome Assembly: Learners will study genome assembly techniques and tools, including de novo and transcriptome assembly. They will gain practical skills in using assemblers like Abyss, Spades, and Trinity on high-performance computing environments.
6. 6. Variant Calling and Analysis: This module covers variant calling and analysis techniques, including single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels). Learners will use tools like GATK, FreeBayes, and Strelka and learn to perform variant filtering and functional annotation.
7. 7. Clustering and Population Genomics: Learners will study clustering algorithms and population genomics techniques, including PCA, ADMIXTURE, and D-statistics. They will learn to analyze population structure and infer demographic history using HPC resources.
8. 8. Machine Learning in Genomics: This module introduces machine learning techniques applied to genomics, including classification, regression, and clustering. Learners will use tools like scikit-learn and TensorFlow to build predictive models and gain skills in feature selection and model evaluation.
9. 9. Genomics Data Visualization: Learners will learn how to visualize large genomics datasets using tools like IGV, GenomeBrowser, and Cytoscape. They will gain skills in creating interactive visualizations and interpreting complex genomics data.
10. 10. High-Performance Computing Best Practices: This final module covers best practices for working with genomics data on HPC systems, including job submission, resource management, and performance optimization. Learners will learn to write efficient scripts and use advanced tools like SLURM and PBS to manage their HPC resources effectively.