Fundamentals of Bioinformatics and Biostatistics (Live Online) 

Course Description:

This synchronous online course provides an introduction to the theoretical foundations and practical applications of bioinformatics in the study of genes and proteins. Delivered in a combined lecture and computer lab format, the course is grounded in primary scientific literature and utilizes custom datasets to explore contemporary advancements in genomics and proteomics. Topics include genomic and biomolecular databases, next-generation sequencing technologies, genome and structural informatics, phylogenetics, and transcriptomics, all supported by foundational biostatistical concepts. Emphasis is placed on the use of computational tools and best analytical practices, with hands-on instruction in the R programming language.

• Formulate testable null and alternative hypotheses for biological investigations.
• Design experiments with appropriate biological and technical replication to ensure statistical validity and power.
• Critically evaluate primary scientific literature pertaining to biostatistical and bioinformatics methodologies.
• Conduct gene prediction and sequence alignment, and perform DNA and protein database searches.
• Navigate and utilize major bioinformatics databases and tools, including NCBI, EBI, PDB, UCSC Genome Browser, DAVID Bioinformatics, and STRING for protein-protein interaction networks.
• Explore, visualize, and analyze biological data, including the application of principal component analysis (PCA) using the R programming language.
• Demonstrate understanding of high-throughput sequencing technologies and their applications in genomics.
• Perform phylogenetic and hierarchical clustering analyses to assess intra- and inter-species variation and estimate evolutionary divergence.
• Analyze transcriptomic data using RNA-Seq datasets, applying appropriate computational and statistical techniques.

Course Topics:

• Hypothesis testing, experimental design, biological and technical replicates, and statistical power
• Sequence alignments, gene finding, and differential gene expression analysis.
• Bioinformatics databases (GenBank, EBI, PDB, UCSC genome browser, David Bioinformatics, STRING…)
• Data exploration and visualization using R
• Differential gene expression workflow, data normalization
• Principal component analysis
• Hierarchical Clustering and Phylogenetics / Phylogenomic
• Genome informatics and High Throughput Sequencing
• Structural bioinformatics
• Transcriptomics and RNASeq

Quarter Dates, Schedules, and Deadlines