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CERTIFICATE IN BIOINFORMATICS & BIOMARKERS
The Bioinformatics and Biomarkers Certificate Program introduces students to the newly evolving fields of bioinformatics, proteomics and genomics, and provides a core knowledge of analytical approaches used in these fields. The curriculum is designed to complement coursework and research of students enrolled in the University of Toledo Ph.D. in Biomedical Sciences Program, but also is open to other qualified students. The Program is a joint effort of the University of Toledo Main and Health Science Campuses and Bowling Green State University.
Students enrolled in the Bioinformatics and Biomarkers Certificate Program must take four courses covering the following subject areas:
1) Introduction to the scope of bioinformatics, proteomics and genomics: "BIPG5100/7100 "Fundamentals of BPG"
2) Training in statistical methods used in BPG: BIPG5200/7200"Statistical Methods in Bioinformatics"
3) Handling and manipulation of databases and introduction to computer programming skills needed to manipulate large quantities of nucleic acid and protein sequence data: BIPG6100/7100 "Introduction to Bioinformatic Computation"
4) EITHER: BIPG6400/8400 Applications of BPG, in which faculty members using these methodologies will discuss and demonstrate how these techniques are utilized to solve research problems:
OR: BRIM6200/8200 "Biomarker Discovery, Validation and Implementation", in which faculty provide an overview of biomedical discovery and validation techniques followed by application in selected aspects of individualized medicine.
Upon completion of the Program, students will be prepared to utilize BPG research techniques and be able to interact with specialists in each BPG subdiscipline.
The curriculum consists of four, 3-credit courses (listed below). If you are in a doctoral program, the second (higher) course number is the one for which you should register.
Course No. Course Title & Credits
Fall Year 1
BIPG5100/7100 Fundamentals of Bioinformatics and Proteomics/Genomics, 3 cr
BIPG5200/7200 Statistical Methods in Bioinformatics, 3 cr
Spring Year 1
BIPG6100/8100 Introduction to Bioinformatic Computation, 3 cr
EITHER: BIPG6400/8400 Application of Bioinformatics and Proteomics/Genomics, 3
OR: BRIM6200/8200 Biomarker Discovery, Validation & Implementation, 3cr
*BMSP6340 Current Problems & Research Approaches in Genes and Genomes, or equivalent course approved by the BPG Program, is required for admission into the BPG Certificate Program.
NOTE: Currently enrolled UT Ph.D. in Biomedical Sciences or MSBS students may take individual BPG courses as electives, with permission of the instructor. To receive a Certificate in Bioinformatics, an online application must be submitted (application link at bottom of page).
APPLYING TO THE BIOINFORMATICS AND BIOMARKERS CERTIFICATE PROGRAM:
Applicants who are University of Toledo students must submit the following after applying online (application link below)
1) Official transcripts
2) Statement of purpose
3) One letter of recommendation is required. It must be signed, and on official letterhead. Two additional letters are optional, HOWEVeR, in the event that a student decides to pursue the BPG MSBS degree, it will save time to have the required total of three letters of recommendation already on file.
Applicants who are NOT presently enrolled in classes at The University of Toledo must follow the requirements below, and submit the following through the online application (application link below):
1) Earned baccalaureate and GRE, or earned graduate degree from an accredited college
2) Minimum 3.0 undergraduate GPA; degree in process will be considered.
3) Minimum GRE 300 or minimum requirements set for the UT Ph.D. in Biomedical Sciences or MSBS programs
4) Official transcripts
5) Three (3) letters of recommendation, signed, and on official letterhead
6) Completed electronic application and $45.00 application fee
All applications will be reviewed by the BPG/BRIM Program Admissions Committee.
To receive a Certificate in Bioinformatics and Biomarkers, application must be submitted
online at the following link:
Electronic, online application must be filed ONLY for those seeking a Certificate. If a student takes all four courses, and does not formally apply online, they will NOT receive a Certificate.
PLAN OF STUDY FOR CERTIFICATE PROGRAM:
A Plan of Study form is required by the Graduate School, and should be submitted as soon as possible after being accepted into the Certificate Program. A nearly-completed form (listing all courses) can be found at the link below. Please print out the form, add your name, etc., and indicate the four courses you choose. Students in a doctoral program will choose the 2nd highest number shown for each course.
Plan of Study for Certificate in Bioinformatics & Biomarkers
STUDENT LEARNING OBJECTIVES (SLO's) FOR BIOINFORMATICS CERTIFICATE PROGRAM:
Students completing the Certificate Program WILL BE ABLE TO:
1) Describe mammalian and nonmammalian genome structure and function, including (for example):
1. Coding/non-coding sequence distribution
2. Isochore structure
3. Repeated element distribution
4. Intron/exon structure and distribution
5. Distribution and dynamics of methylation
6. Transcription factor binding sites (for long- and short-range factors)
2) Discuss the processes of genome evolution, including (for example):
1. Mechanisms of mutation
2. Consequences and exploitation of SNPs
3. Fixation of mutations
4. Genetic drift
6. Major theories for the origin of novel genes
7. Nature and basis of codon bias
3) Describe analytic tools associated with systems/bioinformatic approaches, including (for example):
1. Transcriptomics – microarray analysis vs. deep sequencing
2. Proteomic mass spectroscopic methods (identification and abundance)
3. Determining statistical significance in large bioinformatic datasets
4. Determination and structure of interaction networks
5. Functional network maps
4) Understand appropriate statistical analysis of sequence information, including (for example):
1. Probabilistic methods
2. Deterministic methods
3. Machine learning methods, including Support Vector Machines (SVMs)
4. Cluster analysis
5) Describe competent use of existing bioinformatic and statistical software, including (for example):
1. R statistical tools
2. Alignments and their interpretation
3. Phylogenetic analyses
4. Programs to predict genes and transcription factor binding sites
5. Programs to display, predict and analyze 3D biomolecule structures
6) Develop basic PERL programs for bioinformatic analyses, including (for example):
1. Familiarity with the UNIX operating system
2. Writing scripts for extracting information from databases
3. Creating databases
4. Interfacing with supercomputers
7) Describe application of bioinformatic methods to clinical problems, by demonstrating understanding of:
1. Biomarker discovery and validation
2. Major diseases such as cancer, diabetes, and autoimmunity