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Computation in the life sciences

Bioinformatics Learning Framework

Society Learning Goals Articles Sample Learning Objectives
Computation in the life sciences
What is the role of computation in hypothesis-driven discovery processes within the life sciences?
  • Describe the role of bioinformatics in the scientific research method.
  • Explain the necessity for computation in life sciences research.
  • Explain the role of wet-bench techniques in verifying computational results in life science research.
  • Compare and contrast computer-based research with wet-lab research.
  • Read a scientific article and evaluate how bioinformatics methods were employed by the authors to explore a particular hypothesis.
  • Given a scientific question, develop a hypothesis and define computational approaches that could be used to explore the hypothesis.
  • Evaluate the social, legal, and ethical implications of computational approaches to understanding biology.
What computational concepts are important in bioinformatics?
  • Define the term algorithm.
  • Explain the difference between a heuristic (approximate) algorithm and an exact algorithm.
  • Describe the three basic programming structures: sequential, repetition (e.g., while, for) and selection (e.g., if).
  • Use variables and data structures (e.g., lists, arrays, scalars, hash functions).
  • Describe what a regular expression is.
  • Explain the concept of cloud computing.
  • Describe the importance of “big data” in bioinformatics.
  • Describe the means by which “big data” are managed and stored (e.g.
What statistical concepts are important in bioinformatics?
  • Calculate average, median, mode, range, standard deviations for a given data set.
  • Calculate p-values using a t-test for discrete data.
  • Calculate p-values using a z-test for continuous data.
  • Calculate an e-value statistic.
  • Describe the importance of statistical analysis of big data sets.
  • Create a network to illustrate gene interactions.


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