The Praxis Bioinformatics journey leverages AI-powered curation, hands-on data-intensive compute environments (Praxis Cloud), video assessment, quizzes, and expert mentoring to teach students how to use bioinformatics workflows to solve today’s toughest genomics problems. The online program is available 24x7x365 via any web browser or mobile device and includes seven (7) Learning Paths, twenty-eight (28) Skills, and over seventy-five (75) hours of learning material.
Each Skill includes curated resources and assignments that involve the following:
- WATCH (Videos)
- READ (Guides/Articles)
- DISCUSS (Concepts with others via a discussion board)
- DO (Virtual Labs with live access to software applications)
- REVIEW (Quizzes reinforce concepts and demonstrate mastery)
Learning Journey Overview
The Praxis Bioinformatics Learning Journey contains seven (7) Learning Paths. Each Path is a collection of Skills:
- Introduction to Bioinformatics
- Genome Data Mining
- Sequence Alignment
- Structural Bioinformatics
- Functional Genomics
- Systems Biology
Bioinformatics is a dynamic scientific discipline that utilizes computational and statistical methods for solving biological problems. A major theme in bioinformatics is to integrate and understand biological data generated by genome sequencing projects and other high-throughput molecular biology efforts. Bioinformatics tools are developed to reveal fundamental mechanisms underlying the structure and function of macromolecules, biochemical pathways, disease processes, and genome evolution.
Although many bioinformatics problems are solved by computation and programming, this course itself does not require (practical) computer programming. However, it is expected that all of the students learn how to use the available bioinformatics software to solve biological problems. The aim is to emphasize critical thinking: understanding how these tools work in principle and developing a computational mind for biological problems. Major topics to be discussed in the course include: sequence analysis & algorithms, gene annotation, protein sequence analysis, proteomics, gene expression analysis, comparative genomics, genetic networks and pathways analysis, and systems biology.