Outreach & Education

Build A Genome Course

Part of our education and outreach efforts are allocated to the “Build A Genome” (BAG) class at JHU in which students (both American and international) have the hands on experience of the Polymerase Chain Reaction (PCR), DNA cloning and sequencing, nucleic acid structure, and biopolymer synthesis. Students, ranging from high school students to grad students and the occasional postdoc (but >95% undergraduate) master not only methodology, but also fundamentals of Molecular and Synthetic Biology, related computational skills (there is also a specialized computational track in the course), as well as exposure to the economic / entrepreneurial and bioethics sides of SynBio.

Build a Genome webpage: http://syntheticyeast.org/build-a-genome/

Synthetic Yeast 2.0 site: http://syntheticyeast.org/

Courses offered through the
NYU Center for Health Informatics and Bioinformatics

Biostatistics and Bioinformatics for Biologists (Syllabus)

The goal for the Biostatistics and Bioinformatics for Basic Scientists course is to provide an introduction to statistics and informatics methods for the analysis of data generated in biomedical research. Practical examples covering both small-scale lab experiments and high-throughput assays will be explored. The course covers a wide range of topics in a short time so the focus will be on the basic concepts, and in the practical programming exercises the students explore these basic concept and common pitfalls. An introduction of basic Python programming will be given throughout the course and many exercises will involve programming.

Proteomics Informatics (Syllabus)
This course will give an introduction of proteomics and mass spectrometry workflows, experimental design, and data analysis with a focus on algorithms for extracting information from experimental data. The following subjects will be covered in: (1) Protein identification (peptide mass fingerprinting, tandem mass spectrometry, database searching, spectrum library searching, de novo sequencing, significance testing); (2) Protein char­acterization (protein coverage, top-down proteomics, post-translational modifications, protein processing and degradation, protein complexes); (3) Protein quantitation (metabolic labeling - SILAC, chemical labeling, label-free quantitation, spectrum counting, stoichiometry, biomarker discovery and verification). Examples will be provided throughout the course on how the different approaches can be applied to investigate biological systems. The class will be structured to include hands-on practical techniques for analyzing relevant proteomics datasets.

Courses offered through the
JHU Center for Computational Genomics

Inferring Phylogenies in Cancer

Inference of phylogenetic trees has been used lately in several published studies in several neoplastic conditions to describe evolutionary histories of individual neoplasms (neoplasms occurring in an individual patient). An inferred phylogenetic tree can typically answer questions about the temporal order of acquisition of epi/genomic changes in a single neoplasm. Also, the inferred topology of the phylogenetic tree can typically answer questions about the evolutionary history of cell lineages that rise and fall in frequency over time, and over the course of treatment. The lecture part of this short module will go over nine studies where phylogenetic trees have been inferred using various types of –omics data obtained from individual neoplasms. The hands-on part will show the student how to infer and interpret a phylogeny from a cancer dataset using PHYLIP and BEAST software packages.

Gene Expression Analysis
This course will cover the basic concepts of genomic analysis, and is designed for students with a background in biology and/or biostatistics, and interest in basic or clinical/translational research. The goal is to provide a general orientation and pointers to simple and effective methodologies for analyzing genomic data in these contexts. Specific topics will include: Part 1: Read and explore gene expression data: a) measurement technologies, preprocessing, and quality control; Part 2: Differential gene expression analysis: a) gene annotation; b) identification of features associated with phenotypes; c) analysis by gene sets and pathway.

Computational Analysis of Sequencing Data
This is an introduction to the array of computational methods, many new but some old, that underlie popular software used today. We will cover the computational ideas behind these tools, describe what makes them different from or similar to each other, and address questions on how to interpret their output.

Introduction to Unix
Understanding the Unix environment and interface is critical to using modern bioinformatics programs. This course will cover the basics of using Unix, including how to find help with any Unix command.

Introduction to Python

Awk, Sed, and Shell scripting
An introduction to the classic and essential Unix tools.