Developing an undergraduate bioinformatics course

Scientfc thinking with computatonal skillsDeveloping an undergraduate bioinformatcs course

Kate L Hertweck, Ph.D, Department of Biology, The University of Texas at Tyler

ABSTRACT

A challenge for undergraduate science educators ismeetng increasing demands for students to handle,analyze, and interpret large-scale biological data.Bioinformatcs (BIOL 4306/4106) is a newly developedcourse being taught in spring 2015 through theDepartment of Biology. During the lecture and lab,students are taught how to integrate biological contentknowledge with computatonal methods. The use oftechnology and focus on critcal scientfc thinking makethe class challenging but tractable for students from avariety of educatonal backgrounds. Assessments aredesigned to help students develop practcal,transferable skills they can apply to a variety ofprofessions.

IMPLEMENTATION● Lecture highlights concepts and theory related to

analysis of large biological datasets, like fromgenomic sequencing projects.

● Lab focuses on technical skills, like implementngcomputer code and statstcal analyses, to solveproblems.

● Assessment is based on homework and projects,rather than exams, to focus on applicaton of skills.

● All materials developed for lab are publicly available:htps://github.com/BioinformatcsSpring2015

● Another new class, Bioinformatcs for Research, willbe ofered in Fall 2015 to assist Biology graduatestudents in performing scientfc research.

● Describe the scope of bioinformatcs research andapplicatons

● Design and implement bioinformatcs pipelines to answerpre-defned questons from a variety of biologicaldisciplines

● Validate results from bioinformatcs algorithms usinghypothesis testng, correctng for multple comparisons,etc.

● Characterize the limitatons of data to answer questonsof interests

● Obtain resources to learn new languages and algorithms● Code basic scripts to accomplish the goals above

COURSE OBJECTIVES COURSE CURRICULUM SUMMARYBioinformatcs Framework: fundamental skillsand knowledge for analyzing biological data● Data in biology● Workfows and pipelines● Statstcal inference● Data visualizatonApplied Bioinformatcs: general topics that applylearning from Bioinformatcs Framework● Sequence searching● Phylogenetcs and clustering● Genome assembly● Comparatve genomics

PRE-CLASS SURVEY RESULTS● All students were biology majors● All students agreed (strongly or moderately) that

they wanted to improve their data analysiscapabilites

● Some students had previous experience withcomputer programming.

● Most students believed computatonal skillswould be important to their future careers

BIOINFORMATICS

SoftwareDNA sequences Genomic data

Computer programmingStatistics and visualization