" This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution "

Introduction to computational biology including the fundamentals of protein and nucleic acid sequence analysis, phylogenetic analysis, motif finding, hidden Markov models, and 3D structure prediction and modeling. An overview of emerging fields including expression profiling, quantitative image analysis and the modeling of cellular signal transduction networks are also included. Subject designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science but not necessarily both. Two self-study tracks are offered, introducing either basic statistical methods and programming (to biologists) or the fundamentals of molecular biology (to computer scientists). Lectures combine both perspectives to illustrate how computation is having a significant impact on modern biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

This HIBB presentation provides background information on bases, amino acids, proteins, nucleotides and DNA. The presentation then explains what bioinformatics is, lists some examples, and demonstrates some tools. It demonstrates tools which compare parts of human and chimp genes, and illustrate drug resistance analysis and HIV subtype analysis. It then discusses some ethical and clinical aspects to bioinformatics.

This HIBB presentation provides background information on bases, amino acids, proteins, nucleotides and DNA. The presentation then explains what bioinformatics is, lists some examples, and demonstrates some tools. It demonstrates tools which compare parts of human and chimp genes, and illustrate drug resistance analysis and HIV subtype analysis. It then discusses some ethical and clinical aspects to bioinformatics.

This HIBB presentation provides background information on bases, amino acids, proteins, nucleotides and DNA. The presentation then explains what bioinformatics is, lists some examples, and demonstrates some tools. It demonstrates tools which compare parts of human and chimp genes, and illustrate drug resistance analysis and HIV subtype analysis. It then discusses some ethical and clinical aspects to bioinformatics.

This HIBB presentation provides background information on bases, amino acids, proteins, nucleotides and DNA. The presentation then explains what bioinformatics is, lists some examples, and demonstrates some tools. It demonstrates tools which compare parts of human and chimp genes, and illustrate drug resistance analysis and HIV subtype analysis. It then discusses some ethical and clinical aspects to bioinformatics.

This is an introduction to the bioinformatics website provided by the National Center for Biotechnology Information (NCBI). It includes an overview of the basic mission of NCBI and an introduction to the most commonly used biological databases available

This module is an introduction to pairwise sequence alignments between a query sequence and a sequence database, using NCBI's BLAST, and between a query sequence and a genome, using UCSC's BLAT.

This module is designed to introduce the student to PSI-BLAST. PSI-BLAST is a bioinformatics tool, offered through NCBI, for identifying weak, but biologically relevant sequence similarities.

This module is an introduction to the pip, or percent identity plot. The PipMaker tool available through the Penn State Bioinformatics Group web site is used to create a pip using sample reference and query sequences.

Subject assesses the relationships between sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive functional-genomics analyses. Topics include: algorithmic, statistical, database, and simulation approaches; and practical applications to biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations critically assessed. Problem sets and project emphasize creative, hands-on analyses using these concepts.