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Bioinformatics Learning Framework

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Computation in the life sciences
What is the role of computation in hypothesis-driven discovery processes within the life sciences?	An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome Consortium for Active Teaching Infectious Chocolate Joy with a Side of Poissonian Statistics: An activity connecting life science students with subtle physics concepts	show 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?	An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome Consortium for Active Teaching Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence alignment in introductory undergraduate biology courses Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching	show 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. dmptool.org).
What statistical concepts are important in bioinformatics?	An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome Consortium for Active Teaching Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching	show 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.