STUDENT LEARNING OBJECTIVES (SLO'S) for the CERTIFICATE
Students completing the Certificate Program WILL BE ABLE TO:
1) Describe mammalian and nonmammalian genome structure and function, including
(for example):
1. Coding/non-coding sequence distribution
2. Isochore structure
3. Repeated element distribution
4. Intron/exon structure and distribution
5. Distribution and dynamics of methylation
6. Transcription factor binding sites (for long- and short-range factors)
2) Discuss the processes of genome evolution, including (for example):
1. Mechanisms of mutation
2. Consequences and exploitation of SNPs
3. Fixation of mutations
4. Genetic drift
5. Phylogenetics
6. Major theories for the origin of novel genes
7. Nature and basis of codon bias
3) Describe analytic tools associated with systems/bioinformatic approaches, including
(for example):
1. Transcriptomics – microarray analysis vs. deep sequencing
2. Proteomic mass spectroscopic methods (identification and abundance)
3. Determining statistical significance in large bioinformatic datasets
4. Determination and structure of interaction networks
5. Functional network maps
4) Understand appropriate statistical analysis of sequence information, including
(for example):
1. Probabilistic methods
2. Deterministic methods
3. Machine learning methods, including Support Vector Machines (SVMs)
4. Cluster analysis
5) Describe competent use of existing bioinformatic and statistical software,
including (for example):
1. R statistical tools
2. Alignments and their interpretation
3. Phylogenetic analyses
4. Programs to predict genes and transcription factor binding sites
5. Programs to display, predict and analyze 3D biomolecule structures
6) Develop basic PERL programs for bioinformatic analyses, including (for example):
1. Familiarity with the UNIX operating system
2. Writing scripts for extracting information from databases
3. Creating databases
4. Interfacing with supercomputers
7) Describe application of bioinformatic methods to clinical problems, by demonstrating
understanding of:
1. Biomarker discovery and validation
2. Major diseases such as cancer, diabetes, and autoimmunity