Course Description and Syllabus

Catalog Description: A data driven approach for the computational and statistical understanding and expertise needed to solve bioinformatics problems that you will likely encounter in your research. Topics will include: microarray data analysis, high-throughput sequence data analysis and SNP genotyping analysis as well as some additional specific advanced topics.

Class Meetings: Monday/Wednesday 1:00-2:30 in LSS 440 (IBEST Classroom)

Course Credits: 3 cr.

Prerequisites: One of CS 120 (Computer Science I), Stat 452 (Mathematical Statistics), Biology 456 (Computer Skills for Biologists), or with permission.

Textbook: None

Instructor: Matt Settles

Office: LSS 441C; Phone:885-6051; Email:msettles@uidaho.edu

Teaching Assistant: Hannah Marx

Email:h.marx.h@gmail.com

Course Goals: Following this course the student will be capable of performing their own data analysis project, understanding the technical and statistical tools needed to conduct the analysis with the computational ability to do so, and critically review and implement techniques and methods in publications.

Course Format: The course will be divided into both lecture and lab/workshop sessions.

Topics will include:

  • Expression microarray analysis
  • CGH/CHiP-Chip microarray overview
  • Phylogenetic methods and analysis
  • High-throughput sequence assembly analysis
  • High-throughput sequence mapping analysis
  • RNA-seq overview
  • Metagenomics overview
  • Whole Genome Association Studies overview

Course Grading:A point system will be used for grading. Your semester grade will be based on a standard grading curve (90%, 80%, 70%,…) of the cumulative number of points you have earned by the last day of finals week. There will be 4 projects (each worth a 20 points), and 20 points for either publication reviews (each worth 2 points), or participation in Sequence Analysis Discussion (SAD) group on Fridays, for a total of 100 points.

Each project will be a report on the analysis of provided data (or your own data) using the techniques discussed in class. The reports must be written using Latex or markdown, with embedded R code when appropriate, of the complete analysis. A template with brief introduction is provided in the documents section below.

Publication reviews will be short 1/2 to full page comments on assigned methods papers. A Template is provided in the documents section below.

Most documents associated with this course are available here

Assigned Reading

Marino Marinković, et al. Combining Next-Generation Sequencing and Microarray Technology into a Transcriptomics Approach for the Non-Model Organism Chironomus riparius. PLoS One, 7(10): e48096. 2012. download

Langfelder P, Luo R, Oldham MC, Horvath S Is My Network Module Preserved and Reproducible? PLoS Comput Biol 7(1): e1001057. 2011. download

Settles, M., Coram, T., Soule, T., Robison, B. An improved algorithm for the detection of genomic variation using short oligonucleotide expression microarrays Molecular Ecology Resources 12(6): 1079-1089. 2012. download

Hui-Hsien Chou and Michael HolmesDNA sequence quality trimming and vector removal Bioinformatics 17(12): 1093-1104. 2001. download

Ben Langmead and Steven L Salzberg Fast gapped-read alignment with Bowtie 2 Nature Methods 9, 357–359 (2012). download,Supplemental 1,Supplemental 2