Introduction to Bioinformatics -- Lab Syllabus

Fall 2003 Laboratory Section:
Tuesdays from 3:45 to 5:45 PM in Conradi 223.

The Dirac 499 seminar room, where this course's lectures are held, has the facilities to project live Internet links. Therefore, many lectures include demonstration of biocomputing techniques with local and remote servers. Because of this the optional lab section is not absolutely essential. However, it is strongly recommended, as experience has shown that most student learning occurs when using real data with actual biocomputing software. Students apply theory learned in lecture to experimental settings yielding an advanced understanding of evolution, form, and function.

If students are not taking the Lab, they are encouraged to participate in the non-credit GCG Bioinformatics Workshop Series taught every semester, but they are not required to do so. Steve Thompson is available to assist students in using their own laboratory and/or the Conradi Computing Lab computers for GCG server access, and to help with their term projects throughout the semester.

Week 1, Tues. Aug. 26, 2003:
An introduction to the computing platforms on which the course is taught (pdf).

This includes background information on computers in general, all forms of remote computing, text editing, basics of the UNIX operating system, and the X environment, as well as a brief introduction to the GCG Wisconsin Package and its graphical user interface (GUI) SeqLab.

Week 2, Tues. Sept. 2, 2003:
Molecular databases and how they are organized and accessed (pdf).

Internet sequence and structural databases as well as the on-site GCG sequence databases will be reviewed. Access methods such as those available on the WWW, NetEntrez, and GCG's LookUp will be emphasized but data entry and format conversion are also covered.

Week 3, Tues. Sept. 9, 2003:
Unknown DNA -- rational probe design and analysis -- the "guessmer" (pdf).

How to design and analyze oligonucleotide primers for discovering genes in organisms where they have not been identified when the gene's encoded protein sequence is known in other organisms. Techniques used include basic multiple sequence alignment, consensus creation, back translation, and primer discovery and evaluation.

Week 4, Tues. Sept. 16, 2003:
DNA sequencing -- the GCG fragment assembly system (FAS) -- and restriction enzyme mapping (pdf).

How to get sequencing fragment data from an automated sequencer into the computer and assembled into a continuous sequence and then how to perform restriction enzyme mapping and compositional analysis on that contig for subcloning and other purposes.

Week 5, Tues. Sept. 23, 2003:
Database similarity searching and the dynamic programming algorithm (pdf).

What's available, the methods and algorithms, their limitations, and the significance of their finds. You should never search DNA against DNA, if dealing with coding sequences -- six frame 'blind' translation. Searching methodology -- motifs, substitution matrices, hashing and heuristics, homology versus similarity, dot matrix analysis, pair-wise comparisons, and significance testing.

Week 6, Tues. Sept. 30, 2003:
Gene finding strategies. How are coding sequences recognized in genomic DNA (pdf)?

Searching by signal versus searching by content, i.e. transcriptional/translational regulatory sites and exon/intron splice sites, versus 'nonrandomness,' codon usage; and homology inference. Understanding the concepts and limitations of the methods and differentiating between the approaches.

Week 7, Tues. Oct. 7, 2003:
Advanced multiple sequence alignment -- profile analysis, expectation maximization, and Markov models (pdf).

Lab covers: 1) using MEME to discover hidden motifs; 2) running the progressive, pairwise alignment program PileUp with the SeqLab editor to develop and refine a multiple sequence alignment; 3) creating traditional Gribskov and HMMR profiles for remote similarity searching and further alignment; 4) visualization and annotation techniques for multiple sequence alignments.

Week 8, Tues. Oct. 14, 2003:
Molecular evolutionary phylogenetic inference (pdf).

How to use PAUP* (Phylogenetic Analysis Using Parsimony [and Other Methods], PHYLIP (PHYLogeny Inference Package), and other tools to ascertain and draw phylogenetic trees from multiple sequence alignment datasets. Emphasis is placed on the reliability, congruence, and accuracy of model-based approaches, especially using Maximum Likelihood methods.

Week 9, Tues. Oct. 21, 2003:
Estimating protein secondary structure and physical attributes (pdf).

The various methods, their usefulness, and their limitations are all covered. This includes proteolytic digestion mapping, molecular weight and amino acid composition determination, isoelectric point estimation, hydrophobicity and hydrophobic moment determinations, surface probability and antigenicity mapping, and secondary structure prediction, particularly using methods based on homology inference (e.g. PredictProtein, http://cubic.bioc.columbia.edu/predictprotein/, in North America).

Week 10, Tues. Oct. 28, 2003:
Molecular modeling and visualization (pdf).

Homology modeling combines sequence analysis and molecular modeling to predict three-dimensional structure. Students pick a homologue of their chosen protein that has not had its structure yet solved and use the SwissModel WWW resource (http://www.expasy.org/swissmod/SWISS-MODEL.html) to model the molecule. The theoretical structure is then visualized with the RasMol derivative Protein Explorer (http://www.umass.edu/microbio/rasmol/) to gain insight into the way in which its structure relates to its function. Color coding different physical attributes such as residue charge, hydrophobicity, and secondary structure elements, different representation models, such as alpha-carbon traces, and super-positioning of the model with an actual structure all assist in the interpretation.

After lab students have had their introduction to basic UNIX concepts, utility operations, editing procedures, and molecular databases within the first couple weeks, they decide on a protein of current interest from a list of molecules for which complete structural coordinates are known. They then perform all of the laboratory computer exercises upon that particular molecule. This way they are able to gain experience in all aspects of biocomputing in the course in a project-oriented fashion using the same natural progression as would be used in an actual experimental setting.

Resultant predictive data derived from sequence analysis will no doubt conflict with aspects of the known structural data, but elements of truth will also be found. In this way the strengths and weaknesses of each approach can be better understood and a greater empathy can be found for the tremendous problems encountered in the all-too-common case of a newly discovered gene product without any structural information available. With this approach to computerized molecular biology, students will "come full swing" gaining appreciation for the full biocomputing spectrum available.

This structured exercise tutorial sequence lasts for the first two thirds of the semester, ten weeks. After the laboratory tutorial portion of the course has completed, students participating in the lab then devote scheduled lab sessions to working on their individual research projects. All students will be required to begin dialogue with the lab instructor regarding their project topic early on in the semester and then will be required to submit a proposal as part of the midterm exam. Students are encouraged to choose term projects related to their academic research. This helps to insure excellence by providing a vested interest.