Abstract

Rapid advances in the life sciences and in related information technologies necessitate the ongoing refinement of bioinformatics educational programs in order to maintain their relevance. As the discipline of bioinformatics and computational biology expands and matures, it is important to characterize the elements that contribute to the success of professionals in this field. These individuals work in a wide variety of settings, including bioinformatics core facilities, biological and medical research laboratories, software development organizations, pharmaceutical and instrument development companies, and institutions that provide education, service, and training. In response to this need, the Curriculum Task Force of the International Society for Computational Biology (ISCB) Education Committee seeks to define curricular guidelines for those who train and educate bioinformaticians. The previous report of the task force summarized a survey that was conducted to gather input regarding the skill set needed by bioinformaticians [1]. The current article details a subsequent effort, wherein the task force broadened its perspectives by examining bioinformatics career opportunities, surveying directors of bioinformatics core facilities, and reviewing bioinformatics education programs. The bioinformatics literature provides valuable perspectives on bioinformatics education by defining skill sets needed by bioinformaticians, presenting approaches for providing informatics training to biologists, and discussing the roles of bioinformatics core facilities in training and education. The skill sets required for success in the field of bioinformatics are considered by several authors: Altman [2] defines five broad areas of competency and lists key technologies; Ranganathan [3] presents highlights from the Workshops on Education in Bioinformatics, discussing challenges and possible solutions; Yale's interdepartmental PhD program in computational biology and bioinformatics is described in [4], which lists the general areas of knowledge of bioinformatics; in a related article, a graduate of Yale's PhD program reflects on the skills needed by a bioinformatician [5]; Altman and Klein [6] describe the Stanford Biomedical Informatics (BMI) Training Program, presenting observed trends among BMI students; the American Medical Informatics Association defines competencies in the related field of biomedical informatics in [7]; and the approaches used in several German universities to implement bioinformatics education are described in [8]. Several approaches to providing bioinformatics training for biologists are described in the literature. Tan et al. [9] report on workshops conducted to identify a minimum skill set for biologists to be able to address the informatics challenges of the “-omics” era. They define a requisite skill set by analyzing responses to questions about the knowledge, skills, and abilities that biologists should possess. The authors in [10] present examples of strategies and methods for incorporating bioinformatics content into undergraduate life sciences curricula. Pevzner and Shamir [11] propose that undergraduate biology curricula should contain an additional course, “Algorithmic, Mathematical, and Statistical Concepts in Biology.” Wingren and Botstein [12] present a graduate course in quantitative biology that is based on original, pathbreaking papers in diverse areas of biology. Johnson and Friedman [13] evaluate the effectiveness of incorporating biological informatics into a clinical informatics program. The results reported are based on interviews of four students and informal assessments of bioinformatics faculty. The challenges and opportunities relevant to training and education in the context of bioinformatics core facilities are discussed by Lewitter et al. [14]. Relatedly, Lewitter and Rebhan [15] provide guidance regarding the role of a bioinformatics core facility in hiring biologists and in furthering their education in bioinformatics. Richter and Sexton [16] describe a need for highly trained bioinformaticians in core facilities and provide a list of requisite skills. Similarly, Kallioniemi et al. [17] highlight the roles of bioinformatics core units in education and training. This manuscript expands the body of knowledge pertaining to bioinformatics curriculum guidelines by presenting the results from a broad set of surveys (of core facility directors, of career opportunities, and of existing curricula). Although there is some overlap in the findings of the surveys, they are reported separately, in order to avoid masking the unique aspects of each of the perspectives and to demonstrate that the same themes arise, even when different perspectives are considered. The authors derive from their surveys an initial set of core competencies and relate the competencies to three different categories of professions that have a need for bioinformatics training.

Highlights

  • Rapid advances in the life sciences and in related information technologies necessitate the ongoing refinement of bioinformatics educational programs in order to maintain their relevance

  • The results reported are based on interviews of four students and informal assessments of bioinformatics faculty

  • Analysis of our survey results suggests that there is a common set of desired proficiencies for bioinformaticians

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Summary

Introduction

Rapid advances in the life sciences and in related information technologies necessitate the ongoing refinement of bioinformatics educational programs in order to maintain their relevance. Specific Skills time management, project management, management of multiple projects, independence, curiosity, self-motivation, ability to synthesize information, ability to complete projects, leadership, critical thinking, dedication, ability to communicate scientific concepts, analytical reasoning, scientific creativity, collaborative ability programming, software engineering, system administration, algorithm design and analysis, machine learning, data mining, database design and management, scripting languages, ability to use scientific and statistical analysis software packages, open source software repositories, distributed and high-performance computing, networking, web authoring tools, web-based user interface implementation technologies, version control tools molecular biology, genomics, genetics, cell biology, biochemistry, evolutionary theory, regulatory genomics, systems biology, generation sequencing, proteomics/mass spectrometry, specialized knowledge in one or more domains application of statistics in the contexts of molecular biology and genomics, mastery of relevant statistical and mathematical modeling methods (including experimental design, descriptive and inferential statistics, probability theory, differential equations and parameter estimation, graph theory, epidemiological data analysis, analysis of generation sequencing data using R and Bioconductor) analysis of biological data; working in a production environment managing scientific data; modeling and warehousing of biological data; using and building ontologies; retrieving and manipulating data from public repositories; ability to manage, interpret, and analyze large data sets; broad knowledge of bioinformatics analysis methodologies; familiarity with functional genetic and genomic data; expertise in common bioinformatics software packages, tools, and algorithms doi:10.1371/journal.pcbi.1003496.t001 database management languages (e.g., Oracle, PostgreSQL, and MySQL), and scientific and statistical analysis software (such as R, S-plus, MATLAB, and Mathematica). This is a task that will require community participation, and it is our hope that a shared desire to identify best practices and the benefits of having a program listed in a central repository will encourage broad participation

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