Sunday, May 28, 2017

NIBLSE, bioinformatics core competencies

This set of bioinformatics core competencies for undergraduate life scientists is informed by the survey results of more than 1,200 people, analysis of 90 syllabi addressing bioinformatics across institutions and diverse departments, and discussion among experts across academia and industry. The bulleted lists contain examples illustrating the competencies.

  1. Explain the role of computation and data mining in addressing hypothesis-driven and hypothesis-generating questions within the life sciences: It is crucial for students to have a clear understanding of the role computing and data mining play in the modern life sciences. Given a traditional hypothesis-driven research question, students should have ideas about what types of data and software exist that could help them answer the question quickly and efficiently. They should also appreciate that mining large datasets can generate novel hypotheses to be tested in the lab or field.
    • What hypotheses can one ask based biometric data being compiled (Fitbit, Google, etc.)
    • Understand the role of various databases in identifying potential gene targets for drug development
  2. Summarize key computational concepts, such as algorithms and relational databases, and their applications in the life sciences: In order to make use of sophisticated software and database tools, students must have a basic understanding of the underlying principles that these tools are based upon. Students are not expected to be experts in multiple algorithms or sophisticated databases, but currently the vast majority of life sciences majors never take a programming or database course, and have essentially zero exposure to how these tools work. This must change.
    • Be exposed to how data is organized in relational databases
    • Be able to modify the search parameters to achieve biologically meaningful results
    • Understand underlying algorithm(s) employed in sequence alignment (e.g. BLAST)
  3. Apply statistical concepts used in bioinformatics: Many biology curricula contain statistics, either as a standalone biostatistics course or as part of other courses such as capstone research courses. The primary distinction with regard to bioinformatics has to do with the statistics of large datasets and multiple comparisons.
    • Drug trials: Interpretation of well designed drug trial data
    • Transcriptomics: Understand the statistical modelling used to identify differentially expressed genes; Understand how genes implicated in cancer are identified using panels of sequenced tumor and WT cell lines or biopsies
    • Sequence similarity searching: Understand that there is a probability of finding a given sequence similarity score by chance (the p-value); The size of the database searched affects the probability that they would see that particular score in a particular search (the expectation, or e-value).
  4. Use bioinformatics tools to examine complex biological problems in evolution, information flow, and other important areas of biology: This competency is written broadly so as to encompass a variety of problems addressed using bioinformatics tools, from understanding the evolutionary underpinnings of sequence comparison and homology detection, to the distinctions between genomic sequences, RNA sequences, and protein sequences, to the interpretation of phylogenetic trees. We want to emphasize that bioinformatics tools can be used to teach existing parts of the curriculum such as the central dogma or phylogenetic relationships, thus integrating the bioinformatics into the curriculum as opposed to adding it on as an addition to an already overfull curriculum (and thus forcing decisions about what topic to remove to make room). The point of saying “complex” biological problems is that students should be able to work through a problem with multiple steps, not just perform isolated tasks.
    • Employ gene ontology tools (e.g., Mapman, GO, KEGG).
    • Understand protein sequence, structure, and function, using a variety of tools
    • Understand gene structure, genomic context, alternative splicing using genome browsers
    • Understand concept of homology
  5. Find, retrieve, and organize various types of biological data: Given the numerous and varied datasets currently being generated from all of the ‘omics fields, students should develop the facility to: identify appropriate data repositories; navigate and retrieve data from these databases; and organize data relevant to their area of study (in flat files or small local stand-alone databases).
    • Store and interrogate small datasets using spreadsheets or delimited text files.
    • Navigate and retrieve data from genome browsers
    • Retrieve data from protein and genome databases (PDB, UniProt, NCBI)
  6. Explore and/or model biological interactions, networks and data integration using bioinformatics: Modeling of biological systems at all levels, from cellular to ecological, is being facilitated by technological (e.g., sequencing, biochemical, genetics) and algorithmic advances. These models provide novel insights into the perturbations in systems causative of disease, interactions of microbes with various eukaryotic systems, and how metabolic networks respond to environmental stresses. Students should be familiar with the techniques used to generate these analyses, have the ability to interpret the outputs, and use the data to generate novel hypotheses.
    • Cell Biology: predict impact of gene knockout on cell-signaling pathway
    • Transcriptome: Analysis of transcriptomic data (RNA-Seq) available from SRS using Galaxy
    • Ecological: Analysis of microbial sequence data using QIIME on Galaxy
  7. Use command-line bioinformatics tools and write simple computer scripts: The majority of the datasets students should be familiar with and be able to interact with (e.g., genomic and proteomic sequences, BLAST results, RNASeq and resulting differential expression data) are text files. The most powerful and dynamic way to interact with these datasets is through the command line or shell scripting, both of which are readily acquired skills. Students need to have the flexibility to manipulate their own data, and to create and modify complex data processing and analysis workflows.
    • Write simple unix shell scripts to manipulate files
    • Apply RNASeq analyses using R (STAR, Tophat, DESeq2) to open source data sets (SRS)
    • Build and run statistical analyses using R or Python scripts
    • Run BLAST using command line options
  8. Describe and manage biological data types, structure, and reproducibility: This competency addresses two distinct concerns: 1) each of the varied ‘omics fields produce data in formats particular to its needs, and these formats evolve with changes in technologies and refinements in downstream software; and 2) all experimental data is subject to error and the user must be cognizant of the need to verify the reproducibility of their data. The first concern highlights the requirement for students to develop an awareness of and ability to manipulate different data types given the versioning of formats. The second points to the need for caution, to carry out appropriate statistical analyses on their data as part of normal operating procedures and report the uncertainty of their results, and to provide the relevant information to enable reproduction of their results. Sometimes students have the tendency to assume that anything they retrieve from an online database must be correct; they need to be taught that this is not always the case.
    • Reproducibility: Compare reproducibility of biological replicate data (e.g.transcriptomic data) using statistical tests (Spearman).
    • Formats: Understand the various sequence formats used to store DNA and protein sequences (FASTA, FASTQ); Understand the representation of gene features using Gene Feature Format (GFF) files; Mass-Spec
  9. Interpret the ethical, legal, medical, and social implications of biological data: The increasing scale and penetrance of human genetic and genomic data has greatly enhanced our ability to identify disease-related loci, druggable targets, and potential for gene replacements with developing techniques. However, with this information also comes many ethical, legal, and social questions which are often outpaced by the technological advances. As part of their scientific training, students should debate the medicinal, societal and ethical implications of these information sets and techniques.
    • How does the scientific community protect against the falsification or manipulation of large datasets?
    • Who should have access to this data, and how should it be protected?
    • What are the implications, good and bad, of being able to walk into a doctor’s office and have your genome sequenced and analyzed in minutes?

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