Molecular Biology

DNA, RNA, proteins, metabolites, pathways. This course will focus on the fundamentals of molecular cell biology and will present a bioinformatics approach to investigating functions of genes and proteins and their interactions.
(Adam Ertel, Center for Integrated Bioinformatics, Drexel University)

Bioinformatics Databases

Accession numbers, annotations, navigating online databases. This course will provide an overview of annotation schemes for genes, proteins, molecular interactions and biological pathways. This session will also provide a tutorial for navigating comprehensive online bioinformatics databases such as NCBI, EBI, and KEGG.
(Adam Ertel, Center for Integrated Bioinformatics, Drexel University)

DNA Biology

Nucleotide sequencing, DNA organization. New large scale biological tools have challenged biologists to take a global view of the gene and protein networks underlying cell communication. Topics include de novo nucleotide sequencing and gene finding within novel DNA sequences.
(William Dampier, Center for Integrated Bioinformatics, Drexel University)

DNA Motifs for Regulation of Gene Expression

Transcription factors, DNA sequence motifs, and in silico prediction methods.This session covers key aspects of gene promoters and regulatory elements, including position weight matrix representation of sequence motifs and the identification and comparison of putative binding sequences.
(William Dampier, Center for Integrated Bioinformatics, Drexel University)

Microarrays I

Gene expression microarray overview. This course will present the fundamentals of microarray experiments for high throughput measurement of RNA abundance profiles. An optional wet laboratory session for this course will be conducted at the Penn Microarray Facility *.
(Donald Baldwin, Ph.D., Microarray Core Facility, University of Pennsylvania)

Microarrays II

Basic gene expression microarray analysis. This session will introduce important aspects of microarray data analysis including preprocessing, quality assessment, normalization, and differential analysis. These analysis steps will be carried out using Bioconductor for R: software for statistical computing.
(Michael Gormley, Center for Integrated Bioinformatics, Drexel University)

Microarrays III

Advanced gene expression microarray analysis. Methods including supervised learning, clustering analysis, and gene set enrichment analysis will be introduced. Exercises include identification and analysis of disease subtypes and functional categorization of genes based on their expression profiles.
(Michael Gormley, Center for Integrated Bioinformatics, Drexel University)

Bioinformatics Languages I

Application programming interfaces. This session will guide participants through development of modules/libraries in python for accessing and integrating online bioinformatics databases. This session also includes an introduction to biopython: tools for computational molecular biology.
((Perry Evans, Genomics and Computational Biology, University of Pennsylvania; Yichuan Liu, Center for Integrated Bioinformatics, Drexel University)

Bioinformatics Languages II

Creating statistical models. This session will cover the creation of application of statistical models for sequence annotation and functional prediction. Topics include the application of Markov modes for gene feature prediction and assessing the significance of under-and over-represented sequence motifs.
(Perry Evans, Genomics and Computational Biology, University of Pennsylvania; Yichuan Liu, Center for Integrated Bioinformatics, Drexel University)

Genome Wide Association Studies, an Overview

Genetic disorders, single nucleotide polymorphisms, and meta-analysis. Approaches for combining data from genotyping, metabolomic, and gene expression studies will be explored to provide insight relevant to epidemiologic studies and pharmacogenomics.
(Michael Gormley, Center for Integrated Bioinformatics, Drexel University)