Introduction to Bioinformatics

Introduction to Bioinformatics I: NGS - Data, Methods, Analysis and Implications:
The Center for Biomedical Informatics, Harvard Medical School

COURSE OBJECTIVES
In this course, Introduction to Bioinformatics, students will learn the soups-to-nuts technical, computational, algorithmic and biomedical knowledge databases, genetic variant information systems, and ontology and related resources and tools to conduct practical genome-wide analysis, annotation and interpretation of NGS data. NGS technologies affect nearly every aspect of biomedical sciences and are likely to increase the importance of genetics in medicine. In particular, exome and even whole-genome sequencing will be commonplace, and they will have significant impact on analysis of disease processes and patient treatment. The size of the data generated in this high-throughput sequencing modality is large by any standard. A single run on a next-generation sequencing machine takes between three and six days depending on the platform and generates approximately a terabyte of raw data per experiment. Storing, moving, and analyzing tens and hundreds of terabytes from these platforms poses challenges even for the most seasoned bioinformaticians and computer scientists. For students in biomedical science and health care, exposure to this technology and its application is critical. Besides the wide ranging impact on biomedical science, NGS has created opportunity to explore the vigorous use of genetics in the health care setting. However, the technology, computational analysis, information systems and knowledge annotation systems are as yet far too complicated and of low quality to produce results useful to apply in practice. This course will also introduce the differences and complications between NGS applied in science and NGS applied in medicine.

This course will consist of three learning modules. Three lecturers (Park, Wall, and Tonellato) will present:

• NGS - Technologies and Design: Basic sequencing technology and platforms, coverage and depth of sequencing, base calling and quality scores, error rates and sources of error, design of experiments.
• NGS - Data Analysis and Computation: Genome alignment, methods and tools of simple and complex variant detection, cloud and grid computing.
• NGS – Applications and Implications: RNA-seq, ChIP-seq, miRNA, exome and whole-genome sequencing, cancer genomics, disease association studies, personal medicine and clinical implications.

In addition to the three lecturers, guest lecturers will present special topics and emerging research and medical applications.

All lectures and lecture material will be recorded and deployed to this web site for student access.

LEARNING OBJECTIVES
1. Achieve knowledge of NGS technology, data manipulation and analysis.
2. Methods and tools of high-throughput computational analysis of NGS data
3. Expertise in variant databases, annotation of genetic data, interpretation of NGS data analysis