Lab meeting with Dr. Welch
DCMB Training & Curriculum

Bioinformatics students must be proficient in the areas listed below. For more details on the Bioinformatics courses, please see the course listing page.

For example curriculum tracks to follow, see our Bioinformatics Tracks:
2023-2024 | 2022-2023

If you have any questions about these or other courses, please meet with a faculty adviser for assistance.

For More Information
Areas of Education
Basic requirements
  • Fall courses: BIOINF 500; PIBS 503; BIOINF 527; BIOINF 602/603; BIOINF 504
  • Winter courses: BIOINF 602/603; BIOINF 529
Statistics (Take 2)
  • Fall courses: e.g. BIOSTAT 601; STATS 425; BIOSTAT 521
  • Winter courses: e.g. BIOSTAT 602; STATS 426; BIOSTAT 522
Programming/Computing (Take1)
  • Fall courses: e.g. BIOINF 575; BIOSTATS 615; EECS 453
  • Winter courses: e.g. EECS 545; EECS 553
Biology (at least 3 credit hours)
  • Fall courses: e.g. HUMGEN 545; CDB 530; BIOINF 523; BIOLCHEM 650
  • Winter courses: e.g. BIOLCHEM 640; CANCBIO 554
Advanced bioinformatics (Take 2; at least 1 has to be BIOINF)
  • Fall courses: e.g. BIOINF 540; BIOINF 580; BIOINF 590; BIOINF 593
  • Winter courses: e.g. BIOINF 545; BIOINF 576; BIOINF 597
Other suggested electives
  • Fall courses: e.g. BIOINF 501; BIOSTAT 615; BIOSTAT 523; CDB 530
  • Winter courses: e.g. BIOSTAT 682; HUMGEN 546; BIOSTAT 685


Courses Curriculum

Bioinformatics PhD students must take BIOINF-529. Master’s students may take either one of the following:

  • BIOINF-527: Introduction to Bioinformatics & Computational Biology
  • BIOINF-529: Bioinformatics Concepts and Algorithms
  • BIOINF-575: Programming Laboratory in Bioinformatics

  • BIOINF 576: Tool Development for Bioinformatics
  • BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences
  • BIOINF-585: Deep Learning in Bioinformatics
  • BIOSTAT-615: Statistical Computing
  • BIOSTAT-625: Computing with Big Data
  • EECS-402: Computer Programming For Scientists & Engineers
  • EECS-505: Computational Data Science and Machine Learning
  • EECS-545: Machine Learning
  • EECS-551: Matrix Methods for Signal Processing, Data Analysis and Machine Learning
  • EECS-587: Parallel Computing
  • EECS-592: Foundations of Artificial Intelligence
  • HS-650: Data Science and Predictive Analytics
  • LHS-610: Learning from Health Data: Applied Data Science in Health
  • STATS-507: Modern Data Analysis

Master’s students may take just BIOSTAT 521 or PSYCH 613 to satisfy program requirements. Ph.D. students must take the sequential pair of BIOSTAT 521 + 522 or PSYCH 613 + 614 to satisfy program requirements. Other approved pairs include STATS 425 + 426 and BIOSTATS 601 + 602. If a student takes only 1 of the 2 courses, that is insufficient. In addition, the student must receive a passing grade (“B” or better) in at least the 2nd course.

  • BIOSTAT-521: Applied Biostatistics
  • BIOSTAT-601: Probability & Distribution Theory
  • BIOSTAT-602: Biostat Inference
  • MATH-526: Discrete State Stochastic Processes
  • PSYCH-613: Advanced Statistical Methods
  • PSYCH-614: Advanced Statistical Methods
  • MATH/STATS-425: Introduction to Probability
  • STATS-426: Introduction to Theoretical Statistics
  • STATS-500: Applied Stat I
  • STATS-511: Statistical Inference
  • BIOINF-523: Introductory Biology for Computational Scientists

  • BIOLCHEM-515: Intro Biochem
  • BIOLCHEM-650: Eukaryotic Gene Transcription (*Note: This course is only 2 cr. hrs. It is only approved to satisfy the biology requirement if taken in conjunction with one other course; please speak with an adviser for details.)
  • BIOLCHEM-660: Molecules of life: Protein structure, function and dynamics
  • CDB-530: Cell Biology
  • CDB-550: Histology
  • CBD-581: Developmental Genetics
  • HUMGEN-541: Molecular Genetics
  • HUMGEN-542: Molecular Basis of Human Genetic Disease
  • MCDB-427: Molecular Biology
  • MCDB-428: Cell Biology
  • NEUROSCI-601: Principles Neuroscience II
  • PHRMACOL-501: Chemical Biology
  • PHRMACOL-601: Principles of Pharmacology
  • PHYSIOL-502: Human Physiology

Two courses, among them at least one BIOINF

  • BIOINF-463: Mathematical Modeling in Biology
  • BIOINF-520: Computational Systems Biology in Physiology
  • BIOINF-528: Structural Bioinformatics
  • BIOINF-540: Mathematics of Biological Networks
  • BIOINF-545: High-throughput Molecular Genomic and Epigenomic Data Analysis
  • BIOINF-547: Mathematics of Data (Formerly Probabilistic Modeling in Bioinformatics)
  • BIOINF-551: Proteome and Metabolome Informatics
  • BIOINF-563: Advanced Mathematical Methods for Biological Sciences
  • BIOINF-568: Mathematics and Computational Neuroscience
  • BIOINF 576: Tool Development for Bioinformatics
  • BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences
  • BIOINF-585: Deep Learning in Bioinformatics
  • BIOINF-590: Image Processing and Advanced Machine Learning for Cancer Bioinformatics
  • BIOINF-593: Machine Learning in Computational Biology
  • BIOINF-665/BIOSTAT-665/HUMGEN-665: Statist Popul Genetics
  • BIOSTAT-666: Statistical Methods in Human Genetics
  • BIOSTAT-830: Advanced Topics in Biostatistics
  • BME/EECS-516: Medical Imaging Systems
  • CMPLXSYS-510/MATH-550: Adaptive Dynamics: The mathematics of sustainability
  • CMPLXSYS-530: Computer Modeling (will only count when the topic is relevant to bioinformatics)
  • EHS-674: Environmental and Health Risk Modeling
  • LHS-712: Natural Language Processing for Health
  • STAT-710: Special Topics in Theoretical Statistics I

Most graduate level courses in BIOINF, BIOLOGY, BIOSTATS, EECS, LHS, or STATS can be taken as elective.

  • BIOINF-602: Journal Club (This course is for first-year students who have not taken a journal club before.)
  • BIOINF-603: Journal Club
  • BIOINF-524: Foundations for Bioinformatics

  • BIOSTAT-607: Basic Computing for Data Analytics
  • BIOINF-500: Skills to Succeed in the Bioinformatics Graduate Program and Beyond
  • PIBS-503: Research Responsibility & Ethics
  • BIOINF-504: Rigor and Transparency to Enhance Reproducibility