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Bioinformatics

The science of collecting and analyzing complex biological data.

The CourseSource Learning Framework for Bioinformatics lists the learning goals and objectives relevant to undergraduate biological sciences majors. The framework (below) was developed by Sally Elgin (University of Washington, St. Louis); Mark Pauley (University of Nebraska); Anne Rosenwald (Georgetown University); and Lonnie Welch (Ohio University). 

The following people worked to develop the Bioinformatics Learning Framework:

Sally Elgin (University of Washington, St. Louis); Mark Pauley (University of Nebraska); Anne Rosenwald (Georgetown University); Lonnie Welch (Ohio University)

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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?
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  • 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?
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  • 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?
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  • 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.
DNA - Information Storage [GENOMICS]
Where are data about the genome found (e.g., nucleotide sequence, epigenomics) and how are they stored and accessed?
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  • Describe how nucleotide sequence data are represented (FASTA, FASTQ, GenBank).
  • Describe the nucleotide databases available at NCBI.
  • Describe how the NCBI nucleotide databases intersect with other nucleotide databases (EBI, DDBJ, UniProt, etc.).
  • Compare and contrast the data contained in different nucleotide databases.
  • Search for a sequence record in a nucleotide database with a given accession number.
  • Create a collection of nucleotide sequence records that meet a specified criterion (e.g., gene name or symbol).
  • Determine the DNA methylation state of a particular region of a genome.
  • Describe the types of metadata that accompany sequence data to make for useful biological interpretation (e.g. biological source, accession number, GeneID, journal articles, etc.).
How can bioinformatics tools be employed to analyze genetic information?
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  • Calculate the alignment score between two DNA sequences using a provided scoring matrix.
  • Perform a BLASTN search and interpret the results.
  • Explain the BLASTN algorithm for nucleotide sequence information.
  • Interpret the biological significance of an e-value.
  • Annotate a prokaryotic gene (derive a model).
  • Annotate a eukaryotic gene (derive a model).
  • Create and interpret a multiple sequence alignment (e.g., T-COFFEE, MUSCLE, etc.).
  • For a genomic region of interest (e.g., the neighborhood of a particular gene), use a genome browser to view nearby genes, transcription factor binding regions, epigenetic information, etc.
  • Describe Hidden Markov Models and how they can be used to assess motifs.
RNA - Information Transfer [TRANSCRIPTOMICS]
Where are data about the transcriptome found (e.g., expression, epigenomics and structure) and how are they stored and accessed?
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  • Identify the euchromatin/heterochromatin boundaries, histone states in a given sequence, and the nucleosome modifications
  • Compare and contrast DNA structure at telomeres and centromeres.
  • Describe the RNA databases available at NCBI (e.g., ESTs, UniGene).
  • Describe the types of metadata that accompany sequence data to make for useful biological interpretation (e.g. biological source, accession number, GeneID, journal articles, etc.).
How can bioinformatics tools be employed to examine transfer of genetic information?
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  • Given a microarray or RNA-seq data file, find the set of significantly differentially expressed genes.
  • Perform motif discovery on the promoter regions of a set of genes identified by a ChIP-seq experiment.
  • Use RNA structure prediction programs (e.g., RNASoft, RNAFold, RNAStructure) to evaluate possible structures for an RNA sequence.
  • Identify the possible different splice isoforms possible from a given gene sequence.
Protein - Information in Action [PROTEOMICS]
Where are data about the proteome found (e.g., amino acid sequence and structure) and how are they stored and accessed?
Contribute an article in this area.
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  • Describe how protein sequence data are represented (e.g., FASTA, GenBank, etc.)
  • Describe the different protein databases available at NCBI (sequence, structure, function).
  • Describe how the NCBI databases intersect with other databases (e.g., EBI, DDBJ, UniProt, etc.).
  • Compare and contrast data contained in different databases.
  • Search for a protein record in a database with a given accession number.
  • Create a collection of records that meet a specified criterion (e.g., gene name or symbol).
  • Describe the types of metadata that accompany sequence, structure, and function data to make for useful biological interpretation (e.g. biological source, accession number, UniProt number, journal articles, etc.).
How can bioinformatics tools be employed to examine protein structure and function?
Contribute an article in this area.
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  • Explain the BLASTP, BLASTX, tBLASTn, tBLASTx algorithms for protein sequence information
  • Interpret the biological significance of an e-value.
  • Describe Hidden Markov Models and how they can be used to assess motifs.
  • Query a dataset with a specific protein sequence to learn about potential functions (e.g. Pfam, CDD, SwissProt, UniProt, etc.).
  • View and interpret the structure output from Protein Data Bank (e.g. Cn3D, Jmol, etc.).
  • Propose potential functions for a give protein structure.
  • Explain the outputs from protein-folding algorithms to predict structure from sequence.
  • Understand how protein structures are determined (e.g. NMR, crystallography).
  • Compare and contrast the output from 2-D gel experiments.
  • Analyze the output from mass spectrometry analysis (e.g., use the MASCOT package).
Small Molecules - Cellular Homeostasis [METABOLOMICS & SYSTEMS BIOLOGY]
Where are data about metabolomics and systems biology found and how are they stored and accessed?
Contribute an article in this area.
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  • Describe how metabolomics data are represented (e.g. Human Metabolome Database, METLIN Database, etc.)
  • Describe the different metabolomic databases that are available.
  • Describe the types of metadata that accompany metabolomic data to make for useful biological interpretation.
How can bioinformatics tools be employed to examine flow of molecules within pathways?
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  • Perform a GO analysis to identify the pathways relevant to a set of genes (e.g., identified by a transcriptomic study or a proteomic experiment).
  • Use the KEGG pathway database to look up the interaction network of a pathway.
  • Interpret the data from experiments (e.g., mass spectrometry, nuclear magnetic resonance, etc.) to determine levels of small molecule metabolites.
Ecology and Evolution [METAGENOMICS]
How can bioinformatics tools be employed to examine ecological niches?
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  • Create and interpret a multiple sequence alignment (e.g., T-COFFEE, MUSCLE, etc.).
  • Describe the components of a phylogenetic tree (e.g., root, node, leaf).
  • Explain the various types of phylogenetic trees (e.g., rooted, unrooted).
  • Interpret a phylogenetic tree (e.g., which organism is most closely related to a given organism in the tree)
  • Sketch a phylogenetic tree from its Newick representation.
  • Use bootstrapping to assess the quality of a phylogenetic tree.
  • Create a phylogenetic tree for a set of related sequences (nucleotide or amino acid) (e.g., MEGA).
  • Use pre-existing tools to analyze a metagenomic data set to determine the set of organisms present in a metagenomic sample (e.g., 16s rRNA, Greengenes, mothur, etc.)
Computational Skills
How do biologists employ software development as part of the scientific discovery process?
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  • Write a script to calculate the reverse complement of a nucleotide sequence
  • Write a script to determine reading frames of a nucleotide sequence.
  • Write a script to calculate melting point of double-stranded DNA.
  • Write a script to retrieve the promoter regions for a set of related genes.
  • Write a script to find the longest open reading frame in a given nucleotide sequence
  • Write a script to calculate the reverse complement of a nucleotide sequence.
  • Write a script to convert an RNA sequence to cDNA and to amino acids
  • Write a script to calculate molecular weight and isoelectric point.
  • Write a script to count amino acid frequency.
  • Write a script that compares the relative hydrophilicity/hydrophobicity of two protein sequences.
What higher-level computational skills can be used in bioinformatics research?
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  • Use a spreadsheet to perform simple data analysis.
  • Use a spreadsheet to open, read, parse, modify and output comma-separate (.csv) files that will be ready to use in subsequent tools.
  • Perform elementary statistical analysis on an “omics” dataset (e.g. using Excel or Weka).
  • Perform Input/Output with data files.
  • Interact with remote servers.
  • Construct a bioinformatics pipeline.
  • Use open source libraries and packages (e.g., BioPerl, Biopython, R, BioConductor).
  • Use programs at the Unix/Linux command line to analyze bioinformatics data.
  • Use graph theory to represent data networks.

Bioinformatics

  • Course Editor(s):

    • Srebrenka Robic
      Srebrenka Robic
      Editor Degrees: 

      B.S.  in Mathematics and Mathematical Biology, Beloit College

      Ph.D. in Molecular and Cell Biology, University of California – Berkeley

      About Teaching and Course Source: 

      I have been teaching biology at the undergraduate level for thirteen years. Two following two questions guide my thinking about teaching

      What does it mean to be a biologist in the 21st century?

      How do I prepare my students to become scientists and leaders in the evolving, expanding field of biological science?

      The discipline of biology is expanding rapidly, with new molecular and genomic data being generated at an increasing rate. Boundaries between areas such as molecular biology, genetics and biochemistry are becoming blurred, and interdisciplinary research combining biology with chemistry, physics, mathematics and computational science is becoming a norm at leading research centers. Phrases such as the “human microbiome genomics” and “CRISPR technology” which did not even exist while I was in graduate school, are now regularly found in some of the most exciting new research papers. Clearly, the discipline is constantly evolving – and it is my responsibility to keep my teaching up to date and engaging to my students.

      I have particularly enjoyed designing, developing and and teaching my bioinformatics course. Bioinformatics is absolutely essential for biology of the 21st century.  The rapid development of the field makes it almost impossible to rely on textbooks in teaching. Current, engaging teaching modules, such as those found in CourseSource, are crucial for bioinformatics teaching. The need for new teaching materials is bioinformatics is overwhelming, and I am really excited to help bioinformatics educators disseminate new and exciting teaching materials through CourseSource.

    • Charles Hauser
      Charles Hauser
      Editor Degrees: 

      B.S. Botany, University of Texas, Austin

      Ph.D. Biochemistry and Biophysics, University of Houston

      About Teaching and Course Source: 

      Dr. Hauser received his undergraduate training in botany and chemistry from the University of Texas, Austin.  While there he carried out undergraduate research in the lab of Marshall Johnston, where he carried out a plant survey for land overlooking Redbud Island in Austin.  Upon graduation, he worked with a research group at Shell Research, studying the formulation of solid resins used in a diverse array of products from sailboat hulls to carbon-fiber bicycles. Dr. Hauser returned academics, and receiving his Ph.D. in Biochemistry and Biophysics working with Dr. H. Gray at the University of Houston focused on the enzymology of the nuclease BAL31, a dual-functional endo- and exonuclease. His post-doctoral work at Duke University in the lab of Drs. N.W. Gillham and J.E. Boynton focused on characterization of chloroplast post-transcriptional regulation mechanisms in the unicellular green alga, Chlamydomonas reinhardtii.  Dr. Hauser extended his stay at Duke, where as a research scientist he was involved with building the first set of gene models for the Chlamydomonas genome project, and later with annotation of the draft genomic sequence generated by DOE-JGI in collaboration with Dr. Arthur Grossman at Stanford.   Dr. Hauser, an Associate Professor of Bioinformatics, began at St. Edward's University in 2004 to establish the Bioinformatics Program.

    • William Morgan
      William Morgan
      Editor Degrees: 

      Ph.D., Yale University

      B.S., Cornell University

      About Teaching and Course Source: 

      The life sciences – and life science education – has changed tremendously in the nearly 30 years that I’ve been a college professor. I recognized early on that traditional lectures weren’t the best way to foster student learning and have experimented over the years with a variety of teaching approaches including problem-based learning, case studies, and flipped classrooms. While numerous studies have now demonstrated that active-learning significantly improves student education compared to traditional lectures, a major challenge for instructors continues to be ready access to a sufficiently deep collection of high-quality teaching resources.

      This is why I have been excited by the potential of a journal for peer-evaluated teaching resources ever since I first heard about CourseSource. The peer-review system both ensures the quality of the material collected and properly recognizes authors, who are often overburdened with teaching, research, and service responsibilities, for their valuable contributions. As chair of the Resource Review Committee, I worked with NIBLSE colleagues to establish an “incubator” system where authors can work in small, collaborative groups to refine their bioinformatics teaching materials. As we anticipated, several authors have gone on to submit their final products to CourseSource for publication. Now as a Bioinformatics Course Editor I’m eager to further advance these efforts to share high-quality educational resources with interested colleagues.

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