The graduate certificate in bioinformatics emphasizes the acquisition of biological and computational expertise by supplementing graduate students’ existing background with necessary training in molecular biology, genomics, and computer science. There is a clear need for the development of expertise to analyze the growing amount of biological data generated from genomic, phenotypic, environmental, and other sources. The goal of the certificate is to provide the coursework and a richer academic environment for graduate student to synthesize information across multiple disciplines. The certificate is aimed to prepare highly qualified graduate students who have rigorous multidisciplinary training in molecular biology, genomics, and computer science.
The certificate is aimed at graduate students in engineering, sciences, computer science, and agriculture, although students from other colleges may also find it valuable. The primary objective is to provide students an interdisciplinary training in bioinformatics. Our goal is to develop among the students a critical scientific understanding of bioinformatics, including the biological and computational aspects of algorithm development and implementation.
Student Learning Objectives
The WSU Student Learning Objectives will be addressed in each course within the certificate program. Critical and Creative Thinking, Quantitative Reasoning, Scientific Literacy, and Information Literacy are core skills for graduate students and these skills will be developed across the certificate program. Each course builds on skills learned and expands the Depth, Breadth and Integration of Learning particularly with regard to bioinformatics. The certificate is interdisciplinary in nature and introduces the students to many application areas (for example agriculture, chemistry, medicine) through the courses and instructors. The main idea behind the proposal for a bioinformatics certificate is to direct students into a field that would allow them to synthesize information across multiple disciplines.
Desired learning outcomes
Students earning this certificate will be able to:
Demonstrate a proficiency in basic UNIX skills and analysis of genomics data using common bioinformatics software, relational databases and web resources
Demonstrate a conceptual understanding of the interdisciplinary nature of bioinformatics
Use biological and computational principles to describe algorithm implementation and development
Learn how different fields intersect to contribute to computational biology
Critically explore and evaluate relevant literature and ideas in biology and computer science and foster interdisciplinary thinking and development to address complex biological issues.
Work on complex scientific questions within teams, write interdisciplinary research proposals, provide an oral overview of the proposal to faculty and other students, and constructively and critically review the proposals of other students.
Attend seminars and interact with speakers brought to campus through departmental seminars to create a network and to broaden their thinking about their own disciplinary research.
Create an individualized experience that allows each student to integrate their own disciplinary research with the foundational training from the certificate.
Students must be simultaneously enrolled in a graduate-degree granting program at Washington State University as either full-time or part-time students, or in the case of post-graduate professional, receive permission from the certificate administrators.
A total of twelve credits are required for the graduate certificate.
Three credits are mandatory and the final nine credits will be chosen from a list of electives depending on the background of the student and the cohesion with the research program.
All courses must be approved by the Bioinformatics Certificate committee, prior to acceptance in the certificate program, and will also need the approval from the students’ Masters or PhD committee (on the plan of study).
Students must achieve a GPA of 3.0 (B) or better in each certificate course. Note, some courses require permission of the instructor and prerequisites; it is the responsibility of each student to meet the specific requirements for each course.
Students must take 2 courses outside of their home department (cross-listed courses fulfill this requirement).
Mandatory Course (3 credits):
MBIOS 578 Bioinformatics Computer analysis of protein and nucleic acid sequences, functional genomics and proteomics data; modeling biological networks and pathways. Recommended preparation: Introductory genetics or biochemistry coursework
Elective Courses (9 credits):
BIOLOGY 519 Introduction to Population Genetics Survey of basic population and quantitative genetics
BIOLOGY 534 Modern Methods in Population Genomics Problems and prospects of designing a study with genomic data: from raw data to demography and selection inferences.
BIOLOGY 545 Statistical Genomics See CROP SCI 545.
BIOLOGY 576 Epigenetics and Systems Biology. Current literature based course on epigenetics and systems biology with topics in environmental epigenetics, disease etiology, and role epigenetics in evolutionary biology.
BIOLOGY 566 Mathematical Genetics See MATH 563.
CPT S 570 Machine Learning Introduction to building computer systems that learn from their experience; classification and regression problems; unsupervised and reinforcement learning.
CPT S 571 Computational Genomics Fundamental algorithms, techniques and applications.
CPT S 572 Numerical Methods in Computational Biology Prereq cell biology, probability and statistics, graduate standing in computer science, or permission of the instructor. Computational methods for solving scientific problems related to information processing in biological systems at the molecular and cellular levels.
Crop and Soil Sciences:
CROP SCI 545 Statistical Genomics Concepts and applications in modern breeding programs. (Crosslisted course offered as CROP SCI 545, ANIM SCI 545, BIOLOGY 545, HORT 545, PLP 545)
CROP SCI 555 Epigenetics in Plants Understanding principles of epigenetics in plants with a focus on its role in understanding and improving plant genomes and their adaptation to the changing environment. Recommended preparation: General genetics.
HORT 550 Bioinformatics for Research Foundational knowledge about advanced bioinformatics analyses of next-generation sequencing data. Recommended preparation: Molecular Biology and/or Genetics.
HORT 545 Statistical Genomics see CROP SCI 545
MATH 563 Mathematical Genetics Mathematical approaches to population genetics and genome analysis; theories and statistical analyses of genetic parameters. (Crosslisted course offered as MATH 563, BIOLOGY 566).
MBIOS 503 Advanced Molecular Biology I DNA replication and recombination in prokaryotes and eukaryotes; recombinant DNA methods and host/vector systems; genome analysis; transgenic organisms. Recommended preparation: Introductory genetics and biochemistry coursework.
STAT 523 Statistical Methods for Engineers and Scientists Hypothesis testing; linear, multilinear, and nonlinear regression; analysis of variance for designed experiments; quality control; statistical computing.
STAT 530 Applied Linear Models The design and analysis of experiments by linear models.
MATH/STAT 536 Statistical Computing Generation of random variables, Monte Carlo simulation, bootstrap and jackknife methods, EM algorithm, Markov chain Monte Carlo methods.
STAT 565 Analyzing Microarray and Other Genomic Data Statistical issues from pre-processing (transforming, normalizing) and analyzing genomic data (differential expression, pattern discovery and predictions).
Participating Faculty and Resources:
College of Arts and Sciences (CAS): Omar Cornejo, Biological Sciences; Patrick Carter, Biological Sciences; Joanna Kelley, Biological Sciences; Eric Roalson, Biological Sciences
Voiland College of Engineering and Architecture (VCEA): Shira Broschat, EECS; Ananth Kalyanaraman, EECS
College of Agriculture Human and Natural Resources (CAHNRS): Dorrie Main, Horticulture; Zhiwu Zhang, Crop & Soil Sciences
College of Veterinary Medicine (CVM): Kelly Brayton, Veterinary Microbiology and Pathology; Douglas Call, Global Animal Health; John Wyrick, Molecular Biosciences
Certificate oversight is handled by the Oversight committee:
Kelly Braton, Veterinary Microbiology and Pathology
Omar Cornejo, Biological Sciences
Joanna Kelley, Biological Sciences
Lucia Peixoto, Medicine
Zhiwu Zhang, Crop and Soil Sciences
The certificate is offered on all WSU campuses.
Associate Professor, Biological Sciences
joanna.l.kelley at wsu.edu,
Assistant Professor, Biological Sciences
omar.cornejo at wsu.edu,
How to Earn the Graduate Certificate in Bioinformatics
General rules (Admission requirements)
Admitted Masters or Ph.D. students under the advisement of WSU faculty, and post-graduate professionals who earned their degree in an appropriate field, are eligible to apply for the certificate program. Students who are eligible will notify their department’s graduate committee and their guidance committee of their interest in the certificate. Once the
guidance committee has agreed that it is in the student’s best interest to pursue and complete the certificate, the student will apply to the Bioinformatics Certificate committee. The application will include a statement from the student’s advisor and graduate committee supporting the application.
Applicants should have proficiency in the following: one year of calculus, coursework in probability and statistics (strongly advised as it is required for some courses). It is also advisable for students to have 1 year of computer programming (coursework or experience), but it is not required.