On-line materials, tutorials, lectures etc ...
Amino Acids Page: codes, chemical formulas, physical properties, typical number of contacts in folded structures, peptide bond, pictures.
Kinases and phophorylation motifs database (built by Karthik, joint project with Kathleen Dixon, UC). Structural domains that participate in binding to phospho-peptides and their consensus binding motifs are carefully annotated using literature and bioinformatics tools.
Replication Protein A (RPA) and its interactions (built by Karthik, joint project with Kathleen Dixon, UC).
Fold recognition methods and their applications to annotation of genomic sequences.
Collection of pictures related to my work: a couple of favourite proteins, protein recognition and threading, Molecular Dynamics simulations etc.
My graduate level course: "Introduction to
Lectures '04 (all PowerPoint files because of some elements of animation)
Lecture #1: Overview of the Course: Bioinfomatics as a Field of Study
Lecture #2: From Molecular Processes to String Matching
Lecture #3: Genome Assembly and String Matching
Lecture #4: From Suffix Trees to Approximate String Matching
Lecture #5: Alignment Counting and Dynamic Programming
Lecture #6: Multiple alignment, family profiles, entropy (Perl)
Lecture #7: Clustering and unsupervised learning
Lecture #8: Classification and supervised learning
Lecture #9: K-means vs. k-NN (Perl)
Lecture #10: Applying machine learning techniques to gene prediction (Perl)
Lecture #10: Additional materials: computational gene finding
Lecture #11: Protein structure prediction
Lecture #13: Hidden Markov Models
Lecture #14: From sequence alignment and PsiBLAST to fold recogniotion (hands-on)
Lecture #15: Molecular Dynamics
Lecture #16: Global optimization and Monte Carlo
Taught as Biomedical Eng. course 641, Spring 2004. For other materials, lectures etc. please see >>>
Concise Introduction to Bioinformatics:
Lecture 1: Definitions and general considerations,
Lecture 2/3: Databases and Servers,
Lecture 4/5: Algorithms (sequence alignments and Dynamic Programming, profiles and gene prediction using Hidden Markov Models).
Bioinformatics? Here is a set of problems that may be considered as defining bioinformatics from the computer science standpoint:
1. Pairwise and multiple sequence alignments (dynamic programming, heuristic methods, scoring functions).
2. Hidden Markov Models and applications to protein and RNA structure prediction and gene prediction
3. Molecular evolution (phylogenetic trees, population genetics, statistical models of evolution).
4. Fragment and map assembly (combinatorial approaches to sequencing)
5. Analysis and annotation of genomic sequences
6. Molecular Dynamics and Monte Carlo simulations of biomolecular complexes.
7. Protein folding (homology, threading and ab initio modeling)
8. Molecular biology databases (design and implementation, distributed knowledge).
9. Profiling and comparing genomes using DNA microarrays.
10.Matematical models of interacting networks (chemical kinetics, applications to signaling pathways).
11. High throughput pipelines for genomic sequencing projects.
My paper on Molecular Dynamics for the Encyclopedia of Life Sciences: introductory level, definitions of important notions, review of selected applications.
Molecular Dynamics in MOIL: tutorial for the MOIL package that in fact introduces basic Molecular Dynamics protocols.
Short introduction to the problem of supervised learning.
Rafal's introduction to supervised learning and pattern recognition methods.