BYU Bookstore $ $ To provide practical experience in using computational approaches to address biological problems.

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1 BIO 365 Computational Biology Fall 2012 Section 001: 112 SWKT on T from 1:35 pm 2:50 pm Section 001: 112 SWKT on T from 1:35 pm 2:50 pm Name: Mark Clement Office Phone: (801) Office Location: 3364 TMCB clement@cs.byu.edu Name: Paul Bodily Office Phone: (801) Office Location: 2212 TMCB bioinformaticsta@gmail.com Office Hours: M,W,F 10:00 am to 11:00 am T, 3:00 pm to 4:00 pm Or By Appointment Course Information Description Many approaches to the diagnosis and treatment of human diseases rely on an analysis of DNA sequence data. Investigations into evolutionary relationships between species are often investigated through analyzing sequence data. e large volume of sequence data can only be analyzed with computers. is course will examine fundamental algorithms for obtaining this data and using it to draw conclusions about the underlying biological systems. Students will use the PERL language to write ORF finding, alignment and phylogenetic analysis code. We will also analyze secondary and tertiary structures during the course. Prerequisites CS 240 Material Item Vendor Price (new) Price (used) EXPLORING BIOINFORMATICS Required by C, ST CLAIR, ISBN: Learning Outcomes Overall Objective BYU Bookstore $ $ To provide practical experience in using computational approaches to address biological problems. General Computational Problems in Biology and the Approaches Describe general areas of computational problems in biology (alignment, estimation, tree searches, molecular modeling, gene prediction) and learn about existing computational approaches to these general problems. Computational Tools for Statistical Inference Learn basic computational tools for statistical inference (Bayesian, Maximum likelihood, Markov Chain Monte Carlo, Hidden Markov Chain) Alignment Problems Apply programming skills to alignment problems through the development of novel tools and software for alignment

2 Tree Searching Problems Apply programming skills to tree searching problems through the development of novel tools software Gene Prediction Problems Apply programming tools to gene prediction problems through the development of novel tools and software Database Coding Develop ability to use existing and write new database code to access and manipulate genetic data Grading Scale Grade Percent A 93% to 100% A 90% to 92% B+ 87% to 89% B 83% to 86% B 80% to 82% C+ 77% to 79% C 73% to 76% C 70% to 72% D+ 67% to 69% D 63% to 66% D 60% to 62% E 0% to 59% Grading Policy You are welcome to work with others in developing algorithms, but assignments you turn in should be written by you. Late Policy Late s and Labs lose 10% per day to a maximum of 50% credit. Late exams will not be accepted. Point Breakdown s s 10% #1 Nucleotide Bias 2.5% #2 Bioperl CGI 2.5% #4 Databases 2.5% #3 Alignment 2.5% Laboratories 70% Lab #1 Open Reading Frames 4.38% Lab #2 Genetic Algorithms 10.94% Lab #3 Protein Alignment 10.94% Lab #4 Fragment Assembly 10.94% Lab #5 Hidden Markov Models 10.94% Percent of Grade Lab #6 Secondary Structure Prediction 10.94%

3 s Percent of Grade Lab #7 Phylogeny CGI Script 10.94% Exams 20% Midterm Exam 10% Final Exam 10% Schedule Date Topics Reading s T Aug 28 Introduction to Perl 1.1, 1.2, 1.3, 1.4 Appendix A Installing Perl and Running a Perl Program Read "Tutorial #1 Perl" in Content/Course Materials Examples from class can be found at Aug 30 Perl CGI Appendix B Perl Syntax Read "Tutorial #2 Perl" in Content/Course Materials T 04 Open Reading Frames, Bioperl 2.1, 2.2, 2.3, 2.4 Bioperl Tutorial #1 Nucleotide Bias 06 Genetic Disease 2.5, 2.6, 2.7 #2 Bioperl CGI T 11 Perl Objects Read "Tutorial #3 Perl Objects" in Content/Course Materials Examples from class can be found at Lab #1 Open Reading Frames 13 Global Alignment 3.1, 3.2, 3.3, 3.4, 3.5 Tutorial #4 NeedlemanWunsch T 18 Genetic Algorithms Tutorial Genetic Algorithms Examples from class can be found at #3 Alignment Blast local alignment 3.6, 3.7 Tutorial #4 Blast T 25 Protein Alignment 4.1, 4.2, 4.3, 4.4 gnumap and snp calling Lab #2 Genetic Algorithms

4 Date Topics Reading s 27 Superposition Protein Bioinformatics Ch 9 T D structure visualization Sequence Assembly Pymol Tutorial Paul Bodily Tutorial on ADD Study 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7 illumina, 454 Sequencing, Pacific Biosystems sequencing,cap Assembler,Replication,Transcription, Translation,PCR, PCR machines,cycle Sequencing, Helicos Sequencing, Pyro Sequencing T 09 Midterm Work Session Midterm Exam Opens 11 Using the Supercomputer Supercomputer Documentation putty download T 16 Assemblers Velvet Manual, Velvet Paper, 454 Manual Lab #3 Protein Alignment Midterm Exam Closes 18 Gene Prediction 6.1, 6.2, 6.3, 6.4 Lab #4 Fragment Assembly T 23 Hidden Markov Models Hidden Markov Tutorial 25 Training HMMs Genemark, ghmm tutorial, slides from class T 30 Lab Work Session 01 Secondary Structure Prediction 7.1, 7.2, 7.3, 7.4 Lab #5 Hidden Markov Models T 06 Neural Networks

5 Date Topics Reading s 08 Using Weka Weka Primer, Weka Presentation, Weka download T 13 Databases Examples from class can be found at #4 Databases 3. W Lab Work Session Lab #6 Secondary Structure Prediction T 20 Friday Instruction 22 anksgiving Holiday T 27 Phylogenetics 8.1, 8.2, 8.3, Phylip Phylip Documentation Phylogeny Tutorial T 04 Microarrays 9.1, 9.2, 9.3, Practical Microarray Analysis MAGIC toolkit, Download, Example, Uses, Book Tutorial Lab #7 Phylogeny CGI Script 1. M 10 Final Exam: 112 SWKT 7:00am 10:00am Final Exam T 11 13

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