A genomics learning framework for undergraduates

Abstract

Genomics is an increasingly important part of biology research. However, educating undergraduates in genomics is not yet a standard part of life sciences curricula. We believe this is, in part, due to a lack of standard concepts for the teaching of genomics. To address this deficit, the members of the Genomics Education Alliance created a set of genomics concepts that was then further refined by input from a community of undergraduate educators who engage in genomics instruction. The final genomics concepts list was compared to existing learning frameworks, including the Vision and Change initiative (V&C), as well as ones for genetics, biochemistry and molecular biology, and bioinformatics. Our results demonstrate that the new genomics framework fills a niche not addressed by previous inventories. This new framework should be useful to educators seeking to design stand-alone courses in genomics as well as those seeking to incorporate genomics into existing coursework. Future work will involve designing curriculum and assessments to go along with this genomics learning framework.

Introduction

Genomics is the study of the genome of a single organism or a set of organisms and may include organelle genomes (e.g., mitochondrial, chloroplast, and apicoplast). Genomics as a field has been facilitated by technological advances in genome sequencing (see ref. [1] for historical context). While much has been learned about genes and the relationship between genotype and phenotype, genomics provides a more complete understanding of the polygenic nature of traits and the role that evolution plays in the development of variation, including speciation. The field of genomics is characterized by concepts derived from molecular biology and evolution but derives conclusions from analysis of entire genomes. Genomes include not only the sequences coding for proteins and RNA products (“genes”) but also regulatory sequences. In the case of eukaryotes, much of the genome can be made up of repetitive elements, including transposable elements and their remnants. Repetitive elements have a major impact on both genetic regulation and evolution by affecting DNA packaging. In contrast, prokaryotic genomes are typically minimal, having been stripped down to essential information by selection. In the learning framework presented here, “genome” will mean this complex assembly of coding and non-coding information.

Genomics plays an increasingly valuable part of biological research. For example, a set of 27 papers was published in 2020 that examined the genomes of several thousand different cancers compared to the corresponding normal tissues (the set is described in the flagship paper, ref. [2]). As another example, genomics is increasingly used to evaluate biodiversity and to mitigate biodiversity loss due to habitat loss and climate change [3]. Given this increased use of genomics for research, it is critical that life science undergraduates become familiar with the tenets of genomics so that they are better equipped for graduate study and entry into the workforce. Both academic and applied research fields are increasingly reliant on aspects of data science, and genomics provides a good introduction to “big data’’. Indeed, genomics offers wonderful opportunities for students to engage in authentic, hands-on research [4–6] that can be undertaken anywhere with, at minimum, a computer and an internet connection, allowing more students to become involved in research than is possible with laboratory-based apprenticeship models. Examples of classroom-based undergraduate genomics research programs include the Genomics Education Partnership [7–10], GCAT-Seek [11], Genome Solver [12], the Ciliate Genomics Consortium [13, 14], SEA-PHAGES [15, 16] among others. There are numerous places where faculty can find curriculum grounded in genomics concepts including the Genomics Education Alliance (https://qubeshub.org/community/groups/gea/), the NIBLSE Resource Collection (https://qubeshub.org/community/groups/niblse/), the CourseSource Bioinformatics course (https://qubeshub.org/community/groups/coursesource/), and the Cold Spring Harbor Laboratory DNA Learning Center (https://dnalc.cshl.edu), including the DNA Subway platform (http://www.dnasubway.org).

With the simplification and miniaturization of sequencing technologies and decreases in costs, hands-on genomics increasingly happens in the classroom. A hand-held Oxford Nanopore MinION device generates up to 50 billion DNA bases–the equivalent of 16 human genomes–in 72 hours for less than $1500. Nanopore startup costs are 30–50 times less than other high-throughput sequencing platforms. Nanopore’s mobility, ability to generate long reads (>1 million bases), and increasing accuracy have resulted in its adoption for de novo genome assembly, detection of DNA modifications, RNA sequencing, and metagenomics [17, 18]. The DNA Learning Center, for example, routinely offers Nanopore sequencing summer courses for high school students (https://summercamps.dnalc.org/camps/sequence-a-genome.html).

An overall framework for improving life sciences education, “Vision and Change in Undergraduate Biology Education” (V&C), was developed as a project among numerous stakeholders, including the National Science Foundation and the American Association for the Advancement of Science [19, 20]. While providing a critical starting point, the V&C concepts for biological literacy are broadly descriptive, and although many of the concepts can be illustrated through genomics-related curricula, specific concepts for genomics have not been elaborated like in other life science subdisciplines, including microbiology [21], biochemistry and molecular biology [22], general biology [23–25], immunology [26], neuroscience [27], ecology (https://www.esa.org/4dee/framework/), toxicology [28], pharmacology [29], and bioinformatics [30]. To assist undergraduate educators in designing and implementing a curriculum in genomics, we have identified a collection of key concepts we view as central to an understanding of genomics.

Genomics can be seen as an extension of and overlapping with genetics, molecular biology and/or biochemistry. One of the first concept inventories for genetics was elaborated by Hott and colleagues [31] and an assessment instrument using these concepts was developed by Bowling and colleagues [32]. Another related assessment was developed by Smith, Wood, and Knight [33], then used to assess literacy in genetics [34], with a revision of the learning framework in 2015 [35]. There are several overlapping inventories of concepts and skills for biochemistry and molecular biology, including those by Tansey and colleagues [22], Wright and Hamilton [36], and White and colleagues [37]. Interestingly, Loertscher and colleagues [38] elaborated a set of threshold concepts for biochemistry, ones that “when mastered, represent a transformed and integrative understanding of a discipline without which the learner cannot progress.” Some molecular biology concept inventories are focused on specific topics such as the Central Dogma [39] and meiosis [40].

Similarly, competencies and concept inventories have been elaborated for bioinformatics, a field also closely aligned with genomics. While genomics focuses on the sequence information contained within an organism or set of organisms and its meaning, bioinformatics describes the analysis of such information and the tools that can be applied to the study of genomic data. Recently, a group of undergraduate educators developed a set of competencies; these competencies were then refined by a national survey of life sciences faculty to a set of nine [30]. In addition, a mastery rubric for bioinformatics was developed by Tractenberg and colleagues [41].

However, to our knowledge, no similar framework for genomics specifically has been developed. A search of the literature was conducted at the beginning and the end of our development of the genomics concepts. Although there are disciplinary genomics concept inventories, primarily for healthcare professionals, including, for example, genetics and genomics competencies for nurses [42, 43], none exists for life sciences undergraduates. We therefore attempted to rectify this situation by developing a set of genomics concepts for this population of students.

Methods

Members of the Genomics Education Alliance (GEA; https://qubeshub.org/community/groups/gea) developed the initial genomics concepts in 2019 and 2020, seeded by the learning outcomes of one member’s advanced genomics course, and enhanced with ad hoc feedback from 20 fellow genomics educators from a combination of 2-year, 4 -year, and research-intensive institutions, both public and private, all from the United States. In 2021, additional faculty who teach genomics to undergraduates were surveyed to assess both the specific content of the initial concepts (S1 File) and to determine the relative importance of the concepts to genomics education at various levels (e.g., introductory versus advanced biology majors). The survey was completely anonymous and there was no incentive for participation. Survey participants were recruited through social media (e.g., Twitter), targeted emails to relevant professional groups including the Genomics Education Partnership, Genome Solver, GCAT-Seek, and by asking respondents to send to their contacts (snowball recruiting). The survey was available from 1/14/2021 to 5/24/2021. Consent was implied by the completion of the survey. Sixty-one individuals completed the survey. The survey data was analyzed and reported under IRB protocols at Georgetown (#00006349) and Washington University in St. Louis (#201809064), and deemed to be exempt.

The survey included basic information about the context in which the faculty member teaches genomics (e.g., class size, level of students) and then a Likert scale evaluation of the “importance” (1 = not important, 5 = extremely important) of each concept to undergraduate biology majors’ education. Participants were then asked which of the concepts they include in their own teaching. Finally, participants were asked to provide feedback on the content of the concepts and identify any additional areas that might need to be included. Summary statistics were calculated from the survey results, as well as basic analysis of any correlations between the faculty member’s teaching context and their responses about the importance of a given concept.

The initial framework (S1 Table) was then refined based on the feedback from the faculty community and by the GEA members (n = 17). After feedback and refinement, an additional concept category was added to address the ethical and social implications of genomic research and technology.

We then compared our concept list to established inventories in several related fields, as well as V&C [19] using degrees of freedom analysis [44], which allows for alignment of two inventories at a time. These inventories included genetics inventories, those by Smith, Wood, and Knight [33] and the CourseSource Genetics Learning Framework (https://qubeshub.org/community/groups/coursesource/courses/genetics); biochemistry and molecular biology, those by Tansey et al. [22], Loertscher et al. [38] and the CourseSource Learning Framework for Biochemistry and Molecular Biology (https://qubeshub.org/community/groups/coursesource/courses/biochemistry-and-molecular-biology); and bioinformatics, those by Wilson Sayres et al. [30], Tractenberg et al. [41], and the CourseSource Bioinformatics Learning Framework (https://qubeshub.org/community/groups/coursesource/courses/bioinformatics). Two GEA members independently identified the conceptual overlap between the proposed Genomics Concepts and the other existing inventories. Any differences in overlap assignment were then reconciled through discussion.

Results

Development of the concepts

The Genomics Education Alliance (GEA; https://qubeshub.org/community/groups/gea) was established in 2018 by several undergraduate faculty who had developed hands-on genomics research projects. During discussions about how to best assess genomics learning outcomes, the participants realized that the field lacked an appropriate framework for genomics. Discussions among faculty as well as searches through the literature revealed a lack of agreed-upon genomics concepts. We therefore undertook the creation of a tool for educators.

The initial set of concepts was generated by one faculty member from their Advanced Genomics course, then discussed informally among members of GEA (S1 Table). We also sought input from attendees at the 2020 BIOME Summer Institute (n = 15) [45]. The inventory was intended to collect the set of genomics-related concepts that an undergraduate biology major should understand by the time they graduate. Initially, the concepts were partitioned into five Biological Concepts—three with subheadings (what the genome is and how it functions), and seven Methodological Concepts (how one studies genomes and genomic data) (S1 Table).

This initial set (outlined in S1 Table) was then refined by input via a survey (S1 File) from a broader group of undergraduate biology educators incorporating genomics concepts into their teaching. Sixty-one individuals engaged in genomics education at the college level completed the survey. Of those 61 individuals 30 were or had been engaged in teaching genomics concepts at both the introductory and upper levels, 20 only taught genomics at the upper level, six taught genomics only at the introductory level, and five were not teaching genomics at the time of the survey. At the introductory level, faculty were more likely to be teaching large enrollment classes with less emphasis on genomics. The proportion of the course focused on genomics tended to increase in upper-level classes (Fig 1). We also found that the courses with smaller class sizes tended to include a greater focus on genomics at both the introductory and upper level (S1 Fig).

[Figure omitted. See PDF.]

Class size (A) and percent of course devoted to genomics concepts (B) in introductory (first and second year) courses. Class size (C) and percent of course devoted to genomics concepts (D) in upper-level (third and fourth year) courses.

The faculty indicated the importance of the initially proposed concepts for biology majors to learn. All of the initial concepts were evaluated to be moderately to highly important on average (>3.73 on a 5-point scale, S2 & S3 Figs, S2 Table). Overall, the Biological Concepts (4.29) were rated as more important than the Methodological Concepts (4.17, p<0.011, by Least Squares ANOVA). The four most highly ranked concepts included B.A (4.64, common ancestry), B.D.2 (4.70, central dogma), B.E (4.64, sequence variation) and B.E.6 (4.59, ethics), and the three least important concepts were B.E.3 (3.74, transposable elements) and B.E.5 (3.80, virus genomes), and Methodological Concept M.E (3.89, metagenomics).

We asked the survey respondents whether they taught these concepts in their introductory and upper-level courses. All concepts were taught in at least some courses. Two of the initial Biological Concepts were taught in a significantly higher proportion of advanced relative to introductory courses (B.B & B.D), while all of the Methodological Concepts (except M.G) were taught in a significantly larger proportion of upper-level courses (S4 Fig). Overall, there was also a higher probability that faculty members taught the biological concepts than the methodology concepts (p = 0.003, t-test). Not surprisingly, the probability that a faculty member taught a given concept, in either an introductory or upper-level course, was positively correlated with how “important” the faculty member judged that concept to be (p<0.0001, t-test). Based on the feedback from the survey, the concepts were modified to include new ideas and reorganized into a more logical structure. One major addition, based on strong feedback from the community (10% of respondents), was the creation of a new category, “Ethical Concepts” (ECs), to elevate the importance of teaching ethics as part of genomics (initially listed as biological subconcept B.E.6). Within this category two overarching concepts were developed, one to focus on the impact of genomics on human well-being directly (EC.I and subparts), and the second to focus on genomic impacts on the environment (EC.II and subparts). Biological Concepts about haplotypes (BC.II.B), pangenomes (BC.II.G), and elaboration on cellular genomes of multicellular organisms (BC.V, BC.V.A-BC.V.D) were also added based on community feedback. Finally, based on survey comments, Methodological Concepts about systems biology (MC.V.D), biotechnology (MC.VI, MC.VI.A, MC.VI.B), and elaboration on genomics as a data science (MC.VII.A, MC.VII.B) were added. The final product, an integrated and comprehensive set of core genomics concepts that can guide undergraduate curriculum development, is presented in Table 1. The mapping of the initial concepts sent for survey feedback and of the finalized concepts described in Table 1 is shown in S2 Table.

[Figure omitted. See PDF.]

The list of genomics concepts shown here was developed through several iterations of community input from undergraduate life sciences educators who teach genomics. The intended audience for this list is other undergraduate educators seeking to develop courses in genomics or modify existing courses to contain more genomics elements.

Comparisons to existing learning frameworks

In addition to developing the set of Genomics Concepts reported in Table 1, we also determined the extent to which our genomics concepts captured new information by comparing the new inventory to existing inventories for genetics ([33], https://qubeshub.org/community/groups/coursesource/courses/genetics), biochemistry and molecular biology ([22, 38], https://qubeshub.org/community/groups/coursesource/courses/biochemistry-and-molecular-biology), bioinformatics ([30, 41] https://qubeshub.org/community/groups/coursesource/courses/bioinformatics) as well as V&C ([19]; Table 2). We used a degrees-of-freedom analysis, previously described in the work of Tractenberg [44], in which we constructed grids with the Genomics Concepts as a column and the elements of the inventory to compare to as a row across the top. Each comparison was done independently by two of the authors (LKR and AR) and then compared to each other. Discrepancies were resolved by discussion. Rationale for the assignment of a “match” is given in the appropriate cell of the alignment table.

[Figure omitted. See PDF.]

As an example, we show in Table 2 the reconciled alignment between the genomics concepts and the V&C goals. The V&C goals were designed to elaborate aims for biology education broadly (beyond just genomics, in other words), but nevertheless a number of the genomics concepts resonated with these goals, including an understanding of evolution as one of the core concepts and the use of statistics and modeling as one of the core competencies. We note that all of the V&C Concepts and Competencies are represented in at least one of the genomics concepts and that the genomics concepts are represented in at least one of the V&C Concepts and Competencies, suggesting that the genomics concepts are broadly useful as a way to implement the goals of V&C.

Similarly, we compared the genomics concepts to inventories for disciplines related to genomics. As expected, narrowly focused concept inventories had little overlap with the Genomics Concepts (i.e., the Meiosis Concept Inventory [40], the Central Dogma Concept Inventory [39]). On the other hand, genetics concepts showed relatively large overlaps with the genomics concepts (Supplemental 3 and 4), although at least one of the genetics concept inventories [33] is focused primarily on eukaryotic organisms, including humans, with greater concerns about the effects of mutation on human health than we represent in the genomics concepts. Biochemistry/molecular biology inventories showed intermediate levels of overlap, consistent with the different emphases in this discipline on protein structure and function, energy metabolism, and homeostasis, which are not features of the genomics concepts (S5–S7 Tables). Similarly, bioinformatics inventories showed intermediate levels of overlap, sharing ideas related to computation and algorithms, but not overlapping on specific genomic technologies or the biology of genome structure (S8–S10 Tables). In summary, the comparison to other inventories suggests that the genomics concepts fill a niche not fulfilled by existing inventories or learning frameworks.

Discussion / Conclusion

McKusick and Ruddle [46] adopted, for “the newly developing discipline of mapping/sequencing (including analysis of the information)”, the term Genomics. While in subsequent decades genomics has inserted itself into almost all aspects of life science research, even 30+ years later, instruction in genomics varies widely across different institutions. To help bring undergraduate biology education into better alignment with the current practice of biological and biomedical research, we, a group of educators who teach genomics, have developed and codified a list of genomics concepts for use by other educators. As shown in the pairwise comparisons to inventories developed for other disciplines, this list of concepts is a novel contribution to biology education practices. Importantly, there is a strong alignment of this list of concepts with those laid out in the “Vision and Change in Undergraduate Biology Education” recommendations [19, 20], suggesting that genomics is an excellent way to deliver the goals of V&C to students. The distinctive overlap with concepts relevant to undergraduate genetics, biochemistry, and bioinformatics education (shown in S3–S10 Tables) suggests that classical concepts in biology can be conveyed to undergraduates by aligning with or otherwise integrating genomics concepts.

Thus, we have created a new learning framework through input from a wide community of undergraduate genomics educators, one for genomics education, which we believe will be broadly useful to faculty who are designing coursework in this area. Our future goals will be to develop associated learning objectives and assessments for these concepts. Implementation of these goals for student instruction and faculty professional development will be informed by efforts to implement other inventories. Finally, as the field progresses, we predict that periodic revisions of the concepts will be necessary to keep current.

Supporting information

S1 File. Survey of undergraduate faculty teaching genomics.

https://doi.org/10.1371/journal.pone.0313124.s001

S1 Fig. Relationship between class size and genomics content in courses taught by faculty participating in the inventory survey.

Introductory (A) and Upper-level (B) courses tended to contain more genomics when they were small, and class size tended to decrease in size at the upper relative to the introductory level.

https://doi.org/10.1371/journal.pone.0313124.s002

(TIF)

S2 Fig. Distributions of initial concept importance scores (1 = unimportant, 5 = extremely important) for biology majors to know as scored by faculty participating in the inventory survey.

Initial concepts are provided in S1 Table. A. Initial biological concept A and subparts (A1-A5). B. Initial biological concepts B-D and subparts (D1-D4). C. Initial biological concepts E and subparts (E1-E6). D. Initial methodological concepts A-G.

https://doi.org/10.1371/journal.pone.0313124.s003

(TIF)

S3 Fig. Concept importance variation and correlation for initial concept importance scores (1 = unimportant, 5 = extremely important) for biology majors to know as scored by faculty participating in the inventory survey.

B.# = biological concept, M.# = methodological concept, as listed in S1 Table. A. Average importance scores (+/- 1SE) in rank order from most to least important. All concepts were scored as being moderately to highly important (>3.73), with biological concepts generally ranked more highly. B. Correlations of importance scores for initial concepts clustered based on correlation. Methodological concept scores tended to correlate with other methodology concept scores, and biological importance scores tended to correlate with other biological concept scores.

https://doi.org/10.1371/journal.pone.0313124.s004

(TIF)

S4 Fig. Frequency and proportion of initial genomic concepts were taught in courses lead by faculty participating in the inventory survey.

Initial concepts are provided in S1 Table. All biological concepts were taught in at least some genomics courses, but they were taught more frequently at the upper level (A). When normalized to the number of faculty teaching any genomics content at the introductory or upper level, only biological concepts A.B and A.D were taught in a significantly larger proportion of the upper-level courses (B). All methodological concepts were taught in at least some genomics courses, but they were taught more frequently at the upper level (C). Methodological concepts were also taught in a significantly higher proportion of the upper-level courses when normalized to the number of courses taught, except for concept M.G (D).* p<0.05, ** p<0.01, *** p <0.001, **** p<0.0001 by comparison of two proportions Z-test.

https://doi.org/10.1371/journal.pone.0313124.s005

(TIF)

S1 Table. Initial concepts.

https://doi.org/10.1371/journal.pone.0313124.s006

(XLSX)

S2 Table. Comparison of Initial to final concepts.

https://doi.org/10.1371/journal.pone.0313124.s007

(XLSX)

S3 Table. Genomics concepts aligned with genetics concepts learning goals.

https://doi.org/10.1371/journal.pone.0313124.s008

(XLSX)

S4 Table. Genomics concepts aligned with CourseSource genetics framework.

https://doi.org/10.1371/journal.pone.0313124.s009

(XLSX)

S5 Table. Genomics concepts aligned with foundational concepts in biochemistry and molecular biology.

https://doi.org/10.1371/journal.pone.0313124.s010

(XLSX)

S6 Table. Genomics concepts aligned with threshold concepts for biochemistry.

https://doi.org/10.1371/journal.pone.0313124.s011

(XLSX)

S7 Table. Genomics concepts aligned with the CourseSource biochemistry and molecular biology framework.

https://doi.org/10.1371/journal.pone.0313124.s012

(XLSX)

S8 Table. Genomics concepts aligned with the NIBLSE core competencies.

https://doi.org/10.1371/journal.pone.0313124.s013

(XLSX)

S9 Table. Genomics concepts aligned with the mastery rubric for bioinformatics.

https://doi.org/10.1371/journal.pone.0313124.s014

(XLSX)

S10 Table. Genomics concepts aligned with the CourseSource bioinformatics framework.

https://doi.org/10.1371/journal.pone.0313124.s015

(XLSX)

Acknowledgments

The first iterations of the Genomics Concepts were articulated and discussed at a Genomics Education Alliance workshop in June 2019. We thank our colleagues who participated at that meeting, including Cindy Arrigo (New Jersey City University), Judy Brusslan (California State University Long Beach), Sam Donovan (BIOQuest), Sarah Elgin (Washington University in St. Louis), Matt Escobar (California State University San Marcos), Alexa Sawa (College of the Desert), Naomi Stover (Illinois State University), and Emily Wiley (Claremont McKenna College). We also thank those who participated in the 2020 BIOME Conference for their input as well as the individuals who completed the survey.

We dedicate this paper to our colleague, Vince Buonoccorsi, a tireless advocate for undergraduate education who passed away too soon.

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Citation: Reed LK, Kleinschmit AJ, Buonaccorsi V, Hunt AG, Chalker D, Williams J, et al. (2025) A genomics learning framework for undergraduates. PLoS ONE 20(1): e0313124. https://doi.org/10.1371/journal.pone.0313124

About the Authors:

Laura K. Reed

Roles: Conceptualization, Formal analysis, Visualization, Writing – original draft, Writing – review & editing

Affiliation: Department of Biology, University of Alabama, Tuscaloosa, Alabama, United States of America

Adam J. Kleinschmit

Roles: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation: Department of Natural and Applied Sciences, University of Dubuque, Dubuque, Iowa, United States of America

Vincent Buonaccorsi

† Deceased.

Roles: Conceptualization, Funding acquisition, Methodology, Writing – original draft

Affiliation: Department of Biology, Juniata College, Huntingdon, Pennsylvania, United States of America

Arthur G. Hunt

Roles: Conceptualization, Writing – original draft, Writing – review & editing

Affiliation: Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky, United States of America

ORICD: https://orcid.org/0000-0002-0008-4158

Douglas Chalker

Roles: Conceptualization, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation: Department of Biology, Washington University in St. Louis, St. Louis, Missouri, United States of America

Jason Williams

Roles: Conceptualization, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation: DNA Learning Center, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America

ORICD: https://orcid.org/0000-0003-3049-2010

Christopher J. Jones

Roles: Conceptualization, Writing – original draft, Writing – review & editing

Affiliation: Department of Biological Sciences, Moravian University, Bethlehem, Pennsylvania, United States of America

ORICD: https://orcid.org/0000-0001-5031-3839

Juan-Carlos Martinez-Cruzado

Roles: Conceptualization, Writing – original draft, Writing – review & editing

Affiliation: Department of Biology University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico, United States of America

Anne Rosenwald

Roles: Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing

E-mail: [email protected]

Affiliation: Department of Biology, Georgetown University, Washington, DC, United States of America

ORICD: https://orcid.org/0000-0002-0144-8515

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