Wyoming INBRE Bioinformatics Core
Dept. of Molecular Biology
University of Wyoming
Email: vchhatre@uwyo.edu
Technological advances have led to exponential increase in the size of biological data sets. Handling of these large datasets has become intractable without some level of bioinformatic literacy. Many biologists find that there is a steep learning curve to develop the skills required to explore their datasets effectively because of their size or complexity. This bioinformatics short course includes a rich collection of hands-on instruction and lectures specifically intended to help novice users become comfortable with a range of tools currently used to analyze such data. All coursework will be implemented in linux on local workstations or a remote server.
There is no prerequisite for this course other than a willingness to learn and to work hard throughout the 5 weeks.
(also represents expectations for student performance)
Be familiar with command line interface and working remotely on a high performance computer (i.e., super computer).
Comfortable making, editing, and moving files.
Know how to use basic Linux and R commands.
Write simple scripts to schedule and perform tasks remotely.
Perform and interpret sequence homology searches, sequence alignments, and phylogenetic reconstruction.
Familiarity with handling genomic data generated by NextGen platforms including DNA/RNA Sequence quality assessment and data preparation.
The course is organized into 5 modules
Module 1: Course introduction, linux command line interface & text editors
Module 2: Working on a computer cluster, sequence similarity searches
Module 3: Sequence alignments and phylogenetics
Module 4: Next-generation sequencing and quality assessment
Module 5: Variant discovery and analysis across global human populations
All course materials will be disseminated during (or before) class meetings. These materials will also be available after each class meeting through WyoCourses. Additional reference or working materials will be suggested throughout the course for those interested in exploring topics in a deeper manner.
All grading will be done on an individual basis, even for activities that involve some group work and applies to both undergraduate as well as graduate students. For additional requirements for graduate students, see below. All of the assessments for this course are made during class meetings. Please note: every class meeting is worth 20% of your grade. Thus, you can not pass this class without attending class meetings. Grades will be calculated using the following scale, after conversion of the total number of points acquired to percentages:
A: ≥ 90% B: 80-89% C: 70-79% D: 60-69% F: < 60%
| Grading Component | Undergraduate | Graduate |
|---|---|---|
| Assignments (weekly tutorials) | 70 | 45 |
| Participation (ind & group) | 30 | 30 |
| Research Project | N/A | 25 |
| Total | 100 | 100 |
Graduate Students will choose a research data analysis project with instructors to be completed before November 30, 2018.
Assignments: Working with computers in biology often requires collaboration and input from others. Almost all assignments for this course will be completed during class meetings. At the end of each class meeting you will save the entire commandline session with history command.
Participation: Students are expected to work together to foster an atmosphere of inquiry and respect. While you will be working with computers, creative-problem solving skills will be required to enter into dialogue during class meetings and to complete tasks throughout the 5 weeks.
Attendance Policy, and Policy for Late and Make-up work/examinations: To succeed in this course, it is essential to show up. Twenty points will be subtracted from your grade for every absence that is not University-sponsored or deemed acceptable by the instructor. University-sponsored absences are cleared through the Office of Student Life. Please discuss anticipated absences with the instructor in advance. Make-up work will only be offered for students with acceptable absences.
Academic Honesty: The University of Wyoming is built upon a strong foundation of integrity, respect and trust. All members of the university community have a responsibility to be honest and the right to expect honesty from others. Any form of academic dishonesty is unacceptable to our community and will not be tolerated [from the University Catalog]. Teachers and students should report suspected violations of standards of academic honesty to the instructor, department head, or dean. Other University regulations can be found here.
Disability Statement: If you have a physical, learning, sensory or psychological disability and require accommodations, please let the instructor know as soon as possible. You will need to register with, and provide documentation of your disability to University Disability Support Services (UDSS) in SEO, room 330 Knight Hall.
UW Writing Center: The Writing Center in Coe 302 is able to help writers at any stage of the writing process. With a focus on teaching and learning, the Writing Center is not a “fix-it shop”, but they help writers identify, articulate, and implement improvements and corrections to their writing. Drop in to see if a consultant is available, or schedule an appointment online uwyo.edu/writingcenter.
Please note that with the exception of our first meeting, other events are TENTATIVELY scheduled, because we will work at a pace that is appropriate for the cohort.
Module 1: Course introduction, command line interface
Week 1
Module 2: Computer cluster, sequence similarity searches
Week 2
Connecting to a remote server (Mt. Teton)
Introduction to NCBI and various databases
Understanding the blast output
Novel genes & other databases
Run blast on the command line
Module 3: Sequence alignments and phylogenetics
Week 3
Introduction to R
Lecture: alignments and phylogenetics
Run sequence alignments
Visualize the phylogenetic tree based on alignments
Module 4: Next-generation sequencing and quality assessment of human genomic data
Week 4
Lecture: Introduction to nextgen sequencing
Perform quality assessment on raw sequence data
Perform quality trimming
Repeat assessment to verify quality
Module 5: Analysis of Human Genomic Variation Across Global Populations
Week 5
Lecture: Human population genomics
Study SNP variant data from 1000 Genomes Project
Estimate allele frequencies for specific genetic loci
Use R programming environment for plotting data of allele frequency differences between global human populations