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Resource: Cell Biology

Online Undergraduate Cell Biology Laboratory Resource Curation

Prepared by the Cell Biology Laboratory Working Group
Funding:  Associated Colleges of the South – Advancing Pandemic Pedagogies Initiative

Convener:  Triscia Hendrickson, Associate Professor of Biology, Morehouse College

Working Group Members:
Pamela Hanson, Professor of Biology, Furman University
Centdrika Hurt, Assistant Professor of Biology, Birmingham-Southern College
Elise Kikis, Associate Professor of Biology, University of the South
Jonathan King, Professor of Biology, Trinity University
Mark Lee, Associate Professor of Biology, Spelman College
Laura MacDonald, Assistant Professor of Biology and Health Sciences, Hendrix College
Rebecca Murphy, Associate Professor of Biology, Centenary College of Louisiana

As we enter a new phase in higher education, many of us have been searching for ways to engage students in a virtual learning environment and have found this to be a crucial task for instructors of laboratory courses. The transition from face-to-face instruction has forced us to rethink our approach to teaching labs in the science disciplines. No longer can we solely use the laboratory environment as a training ground for emerging scientists. Rather, we must pivot to online tools and resources through which students can be introduced to and become familiar with the standard approaches used in molecular and cell biology research. Regardless of the physical constraints during this period, we must continue to guide students through the discovery process.

In response to the COVID-19 pandemic, the Associated Colleges of the South brought together colleagues from across the consortium to identify solutions and share resources that address the challenges of moving laboratory instruction into an online or hybrid format.  The Cell Biology Lab Working Group curated existing resources into an online toolkit.

For the purposes of this document, and to be consistent with the extensive crosstalk between biological sub-disciplines, we have framed “cell biology” broadly, including activities that focus on genetics, bioinformatics, and biochemistry. Each item we have curated has been aligned with core concepts and competencies from Vision and Change, which should aid in identifying resources that will help students achieve the learning outcomes for your course(s). Notably, we have focused on curating resources that facilitate active-learning, inquiry, data analysis, and/or authentic research.

To further aid in identification of resources that fit your needs, we have crafted the At-A-Glance spreadsheet, which indicates the duration and course-level for each item.  More detailed curation for each resource summarizes the activities, indicates whether and where related materials can be found, and highlights associated publications.  Because we appreciate the challenges of pivoting to online or hybrid instruction, we have focused largely (though not exclusively) on identifying modules that are ready-to-deploy. To avoid duplicating existing efforts, we have also included links to repositories of virtual lab activities generated by other groups.

We hope you will find this helpful as you prepare for the upcoming term.

At a Glance Resources and Using This Guide

We recognize that individuals may interact with this guide in a variety of ways. To make your efforts more streamlined, we provide a searchable At-a-Glance Grid, where users can search based on topic, duration, division level, technology requirements, and competencies based on the charges outlined by the American Association for the Advancement of Science in their seminal Vision and Change:  A Call to Action.  Additionally, you may wish to search based on type of laboratory exercise. A more detailed description of each exercise is linked in the Table of Contents for easy access, and the table of contents is linked at the bottom of each activity or resource for easy navigation.

 

Table of Contents

  1. Resources and Information on Best Practices for Diversity, Equity, and Inclusion in the Undergraduate Teaching Laboratory Environment
  2. Ready to Deploy Modules
  3. Other Resources

At-a-glance grid for Cell Biology Online Lab Toolkit

Resource*Vision & Change Core Content Area emphasizedVision & Change Competencies emphasizedResource TypeLevelDurationPlatform**Equip.
DEBrowser for R/RStudioSystems, Information FlowQuantitative Analysis, Process of ScienceRepository/
Database/Other		Jr/SrIndividual 2-4 hour lab units up to a semesterDownloadPC only
DNA SubwayInformation Flow, EvolutionProcess of ScienceRepository/
Database/Other		VariousIndividual 2-4 hour lab units up to a semesterWebtablet or PC
Yeast ORFan Gene ProjectEvolution, Structure Function, SystemsProcess of ScienceModules ready to deployVariousAdaptableWebPC only
Genomics Education PartnershipEvolution, Information FlowProcess of ScienceModules ready to deployVariousAdaptableWebPC only
Protein Structure and Genetic DiseasesStructure Function, Information FlowModeling and SimulationModules ready to deployFr/SoFive 2-hour lab periodsWebPC only
Comparative Microbial GenomicsEvolutionProcess of ScienceModules ready to deployJr/SrTwo modules/weeksWebPC only
Constructing and Using a PAM Style Scoring MatrixEvolution, Structure FunctionQuantitative AnalysisModules ready to deployJr/Sr75-minute class/lab meeting plus pre-lab activityWebPC only
GFP Bioinformatics LabEvolution, Structure FunctionModeling and SimulationModules ready to deployFr/So1 lab periodWebPC only
Applied Bioinformatics on Tomato RipeningInformation FlowQuantitative Reasoning, Interdisciplinarity, Process of ScienceModules ready to deployJr/SrFour 4-hour labsWebPC only
Introductory Bioinformatics ModuleInformation FlowProcess of Science, Modeling and SimulationModules ready to deployFr/So90 minutesWebPC only
Introduction to Eukaryotic Genome Analysis in Non-model SpeciesInformation FlowProcess of Science, Quantitative AnalysisModules ready to deployFr/SoMultiple 3 hour laboratory periodsWeb and clusterPC only
Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active TeachingInformation FlowProcess of Science, Quantitative AnalysisModules ready to deployVarious3×3 hour laboratory periodsWebPC only
An undergraduate bioinformatics curriculum that teaches eukaryotic gene structure.Information FlowProcess of ScienceModules ready to deployVarious6-12 hoursWebtablet or PC
Predicting and classifying effects of insertion/deletion mutantsInformation FlowProcess of Science, Modeling and SimulationModules ready to deployVarious6-9 hoursWebtablet or PC
Homologous chromosomes-exploring sex chromosomesInformation FlowProcess of Science, Quantitative AnalysisModules ready to deployFr/So3-6 hoursWebPC only
Tackling “Big Data” with Galaxy-RNASeq Data AnalysisInformation FlowProcess of Science, Quantitative AnalysisModules ready to deployFr/So6-9 hoursWebPC only
A Small-Group Activity Introducing and Using BLASTInformation FlowProcess of ScienceModules ready to deployFr/So1 laboratory periodWebtablet or PC
Engaging Students in a Bioinformatics Activity to Introduce Gene Structure and FunctionInformation FlowProcess of ScienceModules ready to deployFr/So1 laboratory periodWebtablet or PC
Unique Down to Microbes-Inquiry-Based MetagenomicsInformation FlowProcess of Science, Quantitative Analysis, Simulation and ModelingModules ready to deployFr/So6-12 hoursWebPC only
CUREnetVariousProcess of Science, Modeling and Simulation, Quantitative reasoningRepository/
Database/Other		VariousVaries, but most are designed for a 4h lab period. tablet or PC
Library of Integrated Network-based Cellular SignatureStructure FunctionProcess of ScienceRepository/
Database/Other		Jr/Sr WebPC only
Image J/FijiStructure FunctionProcess of ScienceRepository/
Database/Other		Various DownloadPC only
Bio/NeuroinformaticsInformation FlowProcess of ScienceModules ready to deployVariousThree 3-hour lab periods plus 3-hours of lectureDownloadPC only
pClone: Synthetic Biology ToolInformation FlowProcess of ScienceModules ready to deployFr/SoVaries, up to full semesterWebtablet or PC
Western Blot ModuleInformation FlowProcess of ScienceModules ready to deployJr/SrThree 3-hour lab periods plus 1hr of lectureWebPC only
qRT-PCR ModuleInformation FlowProcess of ScienceModules ready to deployJr/SrThree 3-hour lab periods plus 1hr of lectureWebPC only
Genotyping ModuleInformation FlowProcess of ScienceModules ready to deployJr/SrThree 3-hour lab periods plus 1hr of lectureWebPC only
Reporter Constructs ModuleInformation FlowProcess of ScienceModules ready to deployJr/SrThree 3-hour lab periods plus 1hr of lectureWebPC only
Cell CollectiveSystems,  Transformations of Energy & MatterModeling and Simulation, Process of ScienceModules ready to deployVariousIndividual 2-4 hour lab unitsWebtablet or PC
SimBio*Evolution, SystemsModeling and Simulation, Process of ScienceModules ready to deployFr/SoIndividual 2-4 hour lab unitsDownloadPC only
FoldItStructure FunctionModeling and Simulation, Science and SocietyModules ready to deployVariousIndividual 2-4 hour lab unitsDownloadPC only
Allen Integrated CellStructure Function, SystemsProcess of ScienceModules ready to deployVariousIndividual 2-4 hour lab unitsWeb and Downloadtablet or PC
NetLogo WebSystemsModeling and SimulationModules ready to deployVariousVariesWeb and Downloadtablet or PC
LabXchangeInformation FlowModeling and Simulation, Science and SocietyModules ready to deployVariousVariesWeb 
iBiology: Tips for Science Trainees VideosVariousProcess of Science, Ability to CommunicateRepository/
Database/Other		VariousAdaptableWebtablet or PC
Virtual Microscope – FSUStructure and FunctionProcess of ScienceModules ready to deployVariousAdaptableWebtablet or PC
Virtual Microscope – Beckman Imaging GroupStructure and FunctionProcess of ScienceModules ready to deployVariousAdaptableDownload softwarePC only
Network for Integrating Bioinformatics into Life Sciences EducationInformation FlowProcess of Science, Quantitative reasoning, Modeling and SimulationRepository/
Database/Other		VariousVaries  

* All resources are free unless indicated with an asterisk. Price structures can be found in the detailed description of the resource.

** Web indicates browser-based software; download indicates that certain software must be installed on the user’s device

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1. Resources and Information on Best Practices for Diversity, Equity, and Inclusion in the Undergraduate Teaching Laboratory Environment

Resources included in this section provide information on best practices to help all students succeed. Some of the topics addressed are unequal access to technology, hardware, and software, balance between synchronous and asynchronous tools and course materials and how to create an environment that includes and values all students.

For lab experiences that primarily address learning outcomes related to developing students’ skill implementing experimental protocols, digital simulations may allow you to meet or at least approach those objectives. Websites that provide virtual laboratories in which students may simulate experiments and demonstrations include:

Articles that provide more insight on diversity, equity and inclusion during the pandemic

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3. Ready-to-Deploy Modules

Activities and resources included in this section were selected because they featured well-developed materials for use by students and faculty. Furthermore, they exemplified effective pedagogies and/or aligned well with Vision and Change core concepts. Some modules of particular interest to the working group fell into one or more of the following categories:

  • Course-based Undergraduate Research Experiences (CUREs): These modules provide an effective research-based experience online and/or in a hybrid format. Consistent with the defining features of CUREs, these modules foster collaboration, provide student and research goals, and develop critical thinking skills.
  • Modeling and Simulation: These modules support the goals outlined in Vision and Change by allowing students to model individual molecules as well as larger biological systems. The systems biology approach places emphasis on the way small changes in a part of the system can impact the whole.
  • Data Analysis: These modules seek to provide authentic data for investigation and analysis to provide a realistic experience for our students. There are likely numerous other repositories of genomic or microscopic data that may be referenced.

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Yeast ORFan Gene Project

Category: Bioinformatics, CURE

Level: Any

Duration: Customizable, typically a few weeks

Vision and Change

  • Core concepts emphasized: Evolution, Structure-Function, Systems
  • Core competencies emphasized: Process of Science

Synopsis: Although the yeast genome was sequenced over 20 years ago, roughly 10% of yeast genes still have at least one unknown gene ontology (GO) term. This NSF-funded Research Coordination Network for Undergraduate Biology Education (RCN-UBE) provides resources for using bioinformatics to develop hypotheses about yeast genes of unknown function. Modules include analysis of protein structure, evolutionary conservation (multiple sequence alignment), protein localization, and interactions, both genetic and physical.

Strengths: The Yeast ORFan Gene Project exposes students to a broad array of bioinformatics tools and emphasizes the process of science, as students should integrate information they gather to craft a hypothesis about gene function. The project website includes links to YouTube videos showing how to use some of the bioinformatics tools. This project can be highly customized by selecting the module(s) of greatest interest and/or by including a wet-lab experience wherein students test their hypotheses. Some modules involve broadly applicable sites that could be used in the study of genes from other organisms.

Limitations: The modules are currently yeast-focused, and some of the bioinformatics tools included in the modules are not applicable to other organisms. For some genes the tools included in the modules return redundant information, which can dampen student interest; proper framing and/or use of an abbreviated set of bioinformatics sites can help mitigate this minor concern.

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Genomics Education Partnership

Category: Bioinformatics, Genomics, CURE

Level: Varies

Duration: Adaptable/Varies

Vision and Change

  • Core concepts emphasized: Evolution, Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: The Genomics Education Partnership (GEP) is a national collaboration that helps faculty integrate authentic research into their courses. Specifically, GEP has developed and disseminated teacher and student resources to help engage students in the annotation of poorly characterized genomes through comparative analyses. Current projects include evolution of insulin pathways in Drosophila, evolution of venom genes in parasitoid wasps, and expansion of the Muller F Element in Drosophila.

Strengths: Some training materials are openly available and can be used to teach students about the process of annotating genomes. Membership in GEP is free and allows full access to curricular materials, training, and technical support. This project engages undergraduates in authentic research and has been known to result in peer-reviewed publications. GEP materials can be adapted for a wide range of levels and formats. For example, materials can be used in one-off lessons or in semester-long CUREs.

Limitations: The types of scientific questions that can be asked are largely prescribed by GEP leadership.

References:

Shaffer, C. D., Alvarez, C., Bailey, C., Barnard, D., Bhalla, S., Chandrasekaran, C., … & Eckdahl, T. T. (2010). The Genomics Education Partnership: successful integration of research into laboratory classes at a diverse group of undergraduate institutions. CBE—Life Sciences Education, 9(1), 55-69. https://doi.org/10.1187/09-11-0087

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Bio/Neuroinformatics

Category: Bioinformatics, Neurobiology, Data Analysis

Level: Upper division

Duration: Three 3-hour lab periods plus 3-hours of lecture

Vision and Change:

  • Core concepts emphasized: Structure-Function, Information Flow
  • Core competencies emphasized: Process of Science, Interdisciplinarity

Synopsis: This project introduces students to several free databases as well as image analysis via ImageJ. Students connect genotype to phenotype through an examination of mouse neuroanatomy paired with bioinformatics/QTL analysis.

Strengths: A robust set of materials (PowerPoint files, datasets, handouts, etc.) is available for free, though you must register to access the box folder where the materials are housed. This project requires integration of many skills from image analysis to statistics to genetic analysis, highlighting the interdisciplinary nature of modern research.

Limitations: This activity presumes a modest amount of prior knowledge on neuroanatomy. The freely available materials were last updated in 2017, so some data and/or links have changed, so some editing/updating will be required. For example, the activity uses GeneNetwork and provides the link http://www.genenetwork.org/, when the version required for the activity currently resides at http://gn1.genenetwork.org/webqtl/main.py. One part of the analysis relies on SPSS; some institutions may not have this software or may not have sufficient licenses for student access.

Reference:

Grisham, W., Schottler, N. A., Valli-Marill, J., Beck, L., & Beatty, J. (2010). Teaching bioinformatics and neuroinformatics by using free web-based tools. CBE—Life Sciences Education, 9(2), 98-107. https://doi.org/10.1187/cbe.09-11-0079

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Protein Structure and Genetic Diseases

Category: Bioinformatics, Inquiry-based learning

Level: Introductory

Duration: Five 2-hour lab periods

Vision and Change:

  • Core concepts emphasized: Structure-Function, Information Flow
  • Core competencies emphasized: Modeling and Simulation

Synopsis: Over the course of five 2-hour lab periods, students explore protein structure-function relationships that underly genetic diseases. Specifically, they analyze mutant cDNA sequences available at the course web-site. The lab manual includes screenshots of relevant bioinformatics tools, thus helping students know where to enter sequences and where to retrieve relevant information from the sites they visit. Questions embedded in the lab manual prompt students to report out and think about their findings. This project culminates in student presentations.

Strengths: A lab manual (including weekly schedule) and instructor guide are available at the project web-site. This project relies on free, publicly available databases like NCBI, SwissProt, and Online Mendelian Inheritance in Man (OMIM).

Limitations: Students arrive at pre-determined outcomes; although new to them, their findings are not novel to the broader scientific community.

References:

Bednarski, A. E., Elgin, S. C., & Pakrasi, H. B. (2005). An inquiry into protein structure and genetic disease: introducing undergraduates to bioinformatics in a large introductory course. Cell Biology Education, 4(3), 207-220. https://doi.org/10.1187/cbe.04-07-0044

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Comparative Microbial Genomics

Category: Bioinformatics, Genomics, Microbiology

Level: Upper division

Duration: Two modules/weeks

Vision and Change:

  • Core concepts emphasized: Evolution
  • Core competencies emphasized: Process of Science

Synopsis: Two modules guide students through use of whole genome sequence comparisons to study a food poisoning outbreak due to E. coli and the bubonic plague caused by Yersinia pestis. Identification of genomic islands and comparisons of virulence factors aid in development of hypotheses regarding virulence and disease spread.

Strengths: A robust set of materials (PowerPoint files, data files, student instructions, etc.) are readily available as supplemental documents associated with the article describing these modules. This project leverages real world scenarios, a strategy thought to promote student engagement with course materials. The bioinformatics tools used in these modules are free.

Limitations: These activities require Mauve, a free genome alignment tool; at the time of publication Mauve required Java as well as 386 Mb RAM. These prokaryotic-focused modules may not be well aligned with the eukaryotic focus of many cell biology courses.

Reference:

Baumler, D. J., Banta, L. M., Hung, K. F., Schwarz, J. A., Cabot, E. L., Glasner, J. D., & Perna, N. T. (2012). Using comparative genomics for inquiry-based learning to dissect virulence of Escherichia coli O157: H7 and Yersinia pestis. CBE—Life Sciences Education, 11(1), 81-93. https://doi.org/10.1187/cbe.10-04-0057

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Constructing and Using a PAM Style Scoring Matrix

Category: Bioinformatics

Level: Upperclassmen with some coding background

Duration: One 75-minute lab period following completion of a separate pre-lab activity

Vision and Change:

  • Core concepts emphasized: Evolution, Structure-Function
  • Core competencies emphasized: Quantitative Reasoning

Synopsis: This activity aims to increase student understanding of the scoring matrices that underly alignment algorithms.

Strengths: This module is short and can easily be used in conjunction with other activities with bioinformatics tools like BLAST.

Limitations: This activity assumes some pre-existing proficiency with the Python coding language.

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GFP Bioinformatics Lab

Category: Bioinformatics

Level: First semester biochemistry

Duration: One lab period

Vision and Change:

  • Core concepts emphasized: Evolution, Structure-Function
  • Core competencies emphasized: Modeling

Synopsis: This activity introduces students to BLAST, the multiple sequence alignment tool COBALT, and the protein visualization free-ware JSmol. Specifically they find sequence homologues of GFP, align those sequences in COBALT, then visualize the 3-dimensional structure of the protein.

Strengths: This module is short and can easily be adapted for use with other proteins.

Limitations: This activity involves minimal inquiry and does not generate novel data.

Reference:

Rowe, L. (2017). Green Fluorescent Protein-Focused Bioinformatics Laboratory Experiment Suitable for Undergraduates in Biochemistry Courses. Journal of Chemical Education, 94(5), 650-655. https://doi.org/10.1021/acs.jchemed.6b00533

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Applied Bioinformatics on Tomato Ripening

Category: Bioinformatics, Transcriptomics

Level: Upper-level

Duration: Four 4-hour lab sessions

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Quantitative Reasoning, Interdisciplinarity, Process of Science

Synopsis: In this virtual lab, students download and analyze RNAseq data comparing gene expression in ripe vs. unripe tomatoes. Coding is conducted on a combination of student computers and in the cloud; specifically the authors discuss use a virtual machine through NSF-funded Cyverse (https://cyverse.org/). Statistical analyses are conducted in R, and ultimately students generate a heat-map and list of overrepresented functional groups.

Strengths: The module introduces basic command-line computing and bioinformatics while assuming students have little or no experience with programming. All software used in this module is free.

Limitations: Students need access to a computer and some steps of the analyses need hours to run.

Reference:

Madlung, A. (2018). Assessing an effective undergraduate module teaching applied bioinformatics to biology students. PLoS computational biology, 14(1), e1005872. https://doi.org/10.1371/journal.pcbi.1005872

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Introductory Bioinformatics Module

Category: Bioinformatics

Level: Introductory

Duration: 90 minutes

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of Science, Modeling and Simulation

Synopsis: This activity introduces students to the BLAST sequence alignment tool and helps them appreciate how such tools are easy and efficient ways to find the larger context and location of short sequences.

Strengths: Since this activity is so concise, it can easily be incorporated into a regular class meeting or it can be added to other activities for a longer lab period.

Limitations: This activity provides only a very brief introduction to a very limited number of tools.

Reference:

Alaie, A., Teller, V., & Qiu, W. G. (2012). A bioinformatics module for use in an introductory biology laboratory. The american biology Teacher, 74(5), 318-322. 10.1525/abt.2012.74.5.6

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Introduction to Eukaryotic Genome Analysis in Non-model Species

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: Multiple 3-hour long laboratory periods

Vision and Change

  • Core concepts emphasized: Information flow.
  • Core competencies emphasized: Applying the process of science, quantitative analysis.

Synopsis: Tutorial designed to train biology and related majors about tools used for contemporary eukaryotic genomic analysis. This lesson allows students to work through relevant chapters including, but not limited to, quality filtering and trimming of short sequence reads, genome assembly, annotation, variant detection and genome visualization, with an emphasis on the Linux operating system. The tutorial has been used in a variety of settings including a completely independent self-paced tutorial for research students, a partially independent “flipped” classroom, a heavily supported semester-long laboratory, and as the basis for an 18-hour faculty-development workshop.

Strengths: Can be self-paced and entirely online. Accessible across operating systems. In depth training in bioinformatics approaches and techniques.

Limitations: Needs access to a cluster, but faculty may be able to receive accounts through Juniata College. Likely requires a laptop. Some instructor familiarity required.

Reference:

Buonaccorsi, V.P., Hamlin, D., Fowler, B., Sullivan, C., and Sickler, A. 2017. An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome Consortium for Active Teaching. CourseSource. https://doi.org/10.24918/cs.2017.1

https://www.coursesource.org/courses/an-introduction-to-eukaryotic-genome-analysis-in-non-model-species-for-undergraduates-a

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Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: Likely 3 x 3-hour laboratory periods.

Vision and Change

  • Core concepts emphasized: Information flow.
  • Core competencies emphasized: Applying the process of science, quantitative analysis.

Synopsis: Can be used to teach RNAseq analysis. Provides a set of tutorials with modifiable code to allow flexible adaptation to various classroom settings with a relevant tutorial to ease students and faculty into the R statistical environment. The associated materials are directly applicable to both faculty training and classroom settings.

Strengths: Fully published materials that can be adapted for laboratory workflow. Can be self-paced. It’s most ideal to work through the materials first as an instructor yourself.

Limitations: None

Reference:

Peterson, M.P., Malloy, J.T., Buonaccorsi, V.P., and Marden, J.H. 2015. Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching. CourseSource. https://doi.org/10.24918/cs.2015.14

https://www.coursesource.org/courses/teaching-rnaseq-at-undergraduate-institutions-a-tutorial-and-r-package-from-the-genome-0

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An undergraduate bioinformatics curriculum that teaches eukaryotic gene structure.

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: 2-3 hours (replaces 1 laboratory period) but includes additional resources that could be expanded into 2 or 3 laboratory periods.

Vision and Change

  • Core concepts emphasized: Information flow
  • Core competencies emphasized: Process of science

Synopsis: Gene structure, transcription, translation, and alternative splicing are challenging concepts for many undergraduates studying biology. These topics are typically covered in a traditional lecture environment, but students often fail to master and retain these concepts. To address this problem the authors have designed a series of six Modules that employ an active learning approach using a bioinformatics tool, the genome browser, to help students understand eukaryotic gene structure and functionality. Students learn how to use a mirror site of the UCSC Genome Browser created by the Genomics Education Partnership while completing the Modules, which focus on gene structure, transcription, splicing, translation, and alternative splicing.

Strengths: The Modules are supplemented with short videos that illustrate key functionalities of the genome browser and fundamental concepts in processing transcripts. Concepts will be familiar to instructors. Accessible across operating systems. Easy to implement worksheets.

Limitations: Investigative, but not hypothesis-driven.

Reference:

Laakso, M.M., Paliulis, L.V., Croonquist, P., Derr, B., Gracheva, E., Hauser, C., Howell, C., Jones, C.J., Kagey, J.D., Kennell, J., Silver Key, S.C., Mistry, H., Robic, S., Sanford, J., Santisteban, M., Small, C., Spokony, R., Stamm, J., Van Stry, M., Leung, W., Elgin, S.C.R. 2017. An undergraduate bioinformatics curriculum that teaches eukaryotic gene structure. CourseSource. https://doi.org/10.24918/cs.2017.13

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Predicting and classifying effects of insertion/deletion mutants

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: 2 to 3 x 3-hour laboratory periods. Could be spread out over a number of periods or built into class assignments.

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of science

Synopsis: Focuses on how mutations in genes can affect the encoded proteins in multiple ways. In this Lesson, a series of scaffolded exercises provides this opportunity. Students first identify gene sequences from an online database, create their own insertion/deletion mutations, and predict the effects. Students then use a web-based tool to translate and observe the effect of the mutation on protein sequence. Subsequent comparison of predicted and observed effects employs the chi-square test.

Strengths: Accessible across devices and operating systems. Gives good guidance for how it could be adapted for less device-rich circumstances.

Limitations: None.

Reference:

Ross, J.A. 2016. Predicting and classifying effects of insertion and deletion mutations on protein coding regions.

CourseSource. https://doi.org/10.24918/cs.2016.18

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Homologous chromosomes-exploring sex chromosomes

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: 1 or 2 x 3-hour laboratory periods

Vision and Change

  • Core concepts emphasized: Information flow
  • Core competencies emphasized: Process of science, quantitative data analysis

Synopsis: Students will learn about the structure and function of human autosomal and sex chromosomes, view and interpret gene maps, and gain familiarity with basic bioinformatics resources and data through use of the National Center for Biotechnology Information (NCBI) website.

Strengths: Limited instructor training required. Extensive faculty resources available for implementation.

Limitations: Investigative, but not hypothesis driven. Laptop access needed.

Reference:

Metzger, K.J. 2014. Homologous chromosomes? Exploring human sex chromosomes, sex determination and sex reversal using bioinformatics approaches.

CourseSource. https://doi.org/10.24918/cs.2014.5

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Tackling “Big Data” with Galaxy-RNASeq Data Analysis

Category: Bioinformatics

Level: Introductory to Intermediary

Duration: 3 laboratory periods

Vision and Change

  • Core concepts emphasized: Information flow
  • Core competencies emphasized: Process of science, quantitative data analysis

Synopsis: Straightforward and detailed tutorial that guides students through the analysis of RNA sequencing (RNA-seq) data using Galaxy, a public web-based bioinformatics platform.

Strengths: Doesn’t require on-site storage because data is stored in Galaxy, accessible across operating systems. Useful instructor tools and resources. Includes outline of how the exercises could be used with any dataset accessible on the web and provides guidelines for adaptation for hypothesis-driven work.

Limitations: Best done with a laptop.

Reference:

Escobar, M.A., Morgan, W., Makarevitch, I., and Robertson, S.D. 2019. Tackling “Big Data” with Biology Undergrads: A Simple RNA-seq Data Analysis Tutorial Using Galaxy.

CourseSource. https://doi.org/10.24918/cs.2019.13

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A Small-Group Activity Introducing the Using BLAST

Category: Bioinformatics

Level: Introductory

Duration: One laboratory period.

Vision and Change

  • Core concepts emphasized: Information flow.
  • Core competencies emphasized: Process of Science

Synopsis: Basic introduction to BLAST tool.

Strengths: Easy to implement and could be adapted for any gene or topic. Accessible across operating systems and devices.

Limitations: Investigative, but not hypothesis driven.

Reference:

Newell PD, Fricker AD, Roco CA, Chandrangsu P, Merkel SM. A Small-Group Activity Introducing the Use and Interpretation of BLAST. J Microbiol Biol Educ. 2013;14(2):238-243. Published 2013 Dec 2.

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Engaging Students in a Bioinformatics Activity to Introduce Gene Structure and Function

Category: Bioinformatics

Level: Introductory

Duration: One laboratory period.

Vision and Change

  • Core concepts emphasized: Information flow.
  • Core competencies emphasized: Applying the process of science.

Synopsis: This activity is designed to provide secondary and undergraduate biology students to a hands-on activity meant to explore and understand gene structure with the use of basic bioinformatic tools. Students are provided an “unknown” sequence from which they are asked to use a free online gene finder program to identify the gene. Students then predict the putative function of this gene with the use of additional online databases.

Strengths: Easy to implement and could be adapted for any gene or topic. Accessible across operating systems and devices.

Limitations: Investigative, but not hypothesis driven.

Reference:

Barbara J. May. J. Microbiol. Biol. Educ. May 2013 vol. 14 no. 1 107-109. doi:10.1128/jmbe.v14i1.496

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Unique Down to Our Microbes-Inquiry-Based Metagenomics

Category: Bioinformatics

Level: Introductory

Duration: Three laboratory periods.

Vision and Change

  • Core concepts emphasized: Information flow
  • Core competencies emphasized: Process of science, quantitative data analysis, simulation and modeling.

Synopsis: Through this activity, students define terms associated with metagenomics analyses, describe the biological impact of the microbiota on human health, formulate a hypothesis, analyze and interpret metagenomics data to compare microbiota, evaluate a specific hypothesis, and synthesize a conceptual model as to why bacterial populations vary.

Strengths: Open access resources, accessible across operating systems, covers a range of topics relevant to Vision and Change.

Limitations: Most easily done using a laptop.

Reference:

Thomas B. Lentz1,‡, Laura E. Ott2,‡, Sabrina D. Robertson1,‡, Sarah C. Windsor3, Joshua B. Kelley4, Michael S. Wollenberg5, Robert R. Dunn6, Carlos C. Goller1,‡,* J. Microbiol. Biol. Educ. June 2017 vol. 18 no. 2 doi:10.1128/jmbe.v18i2.1284

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Cell Collective

Category: Modeling & Simulation

Level: Fr/So/Jr/Sr

Duration: Pre-developed modules can be completed in a 3-hour lab.

Vision & Change: Systems, Transformations of Energy and Matter

Synopsis:

This is a free, browser-based software that allows students to explore simulation modules in many different areas of molecular biology, such as cellular respiration, the cell cycle, and the lac operon. The simulations can be customized with a teacher account, and they are suitable for any level. Students who are new to the software can follow the instructions, while more advanced students can create their own models and see if their simulation matches realistic ways in which the cells function.

Strengths: Customizable, adjustable dependent on student level, several of their modules have complete lessons with learning outcomes and questions/assessments embedded. Simulations can accompany traditional labs or as standalone assignments. Could potentially be used as part of a CURE if you wanted model-building to be a part of the goals for students.

Limitations: While some of their modules are complete, others don’t always have teaching resources completely ready. There are some new aspects of the teacher account that I’m exploring, but previous iterations did not give you the ability to access student answers for grading. I got around this by creating an assignment that mirrored the instructions/outcomes in our learning management system (Canvas).

References:

  1. Helikar, T., Kowal, B., McClenathan, S. et al.The Cell Collective: Toward an open and collaborative approach to systems biology. BMC Syst Biol 6, 96 (2012). https://doi.org/10.1186/1752-0509-6-96
  2. Helikar T, Kowal B, Rogers JA. A cell simulator platform: the cell collective. Clin Pharmacol Ther. 2013;93(5):393-395. 1038/clpt.2013.41
  3. Helikar T, Cutucache CE, Dahlquist LM, Herek TA, Larson JJ, Rogers JA (2015) Integrating Interactive Computational Modeling in Biology Curricula. PLoS Comput Biol 11(3): e1004131. https://doi.org/10.1371/journal.pcbi.1004131
  4. YouTube: An Introduction to Cell Collective

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SimBio*

Category: Interactive simulations, modeling, simulations

Level: Fr/So

Duration: 1 – 3 hours.

Vision & Change: Evolution, Systems

Synopsis:

SimBio is an interactive, simulation-based software that allows students to watch videos, move things around, adjust inputs, and observe the results. The full suite seems to have modules for IntroBio, Ecology, Evolution, Cell Biology, Environmental Science, Genetics, Conservation Biology, and Physiology.

The Cell biology section has seven tutorial labs and two workbook labs for Cell biology, and six total (three unique) tutorial labs/four unique workbook labs for Genetics.

Cell biology tutorial labs include: Understanding Experimental Design, DNA Explored, Cellular Respiration Explored, Meiosis Explored, Mitosis Explored, Action Potentials Explored, Action Potentials Extended.

Cell biology Workbook labs include Osmosis and Diffusion.

Unique Genetics tutorial labs include: Sickle-Cell Alleles, Genetic Drift and Bottlenecked Ferrets, and Mendelian Pigs.

Genetics Workbook labs include Sickle-Cell Alleles and Genetic Drift and Bottlenecked Ferrets (separate from the tutorial labs), Domesticating Dogs, and The HIV Clock.

The price breakdown from their site is as follows:

  • Labs: $6 each
  • Unlimited Labs: $49
  • Chapters: $10 each
  • Unlimited Chapters & Labs: $89

Strengths: There is an instructor portal where you can invite students and record assessments. The assessments are embedded as part of the activities, and it’s ready to assign out of the box. The interface is intuitive and easy to use. The workbooks provide simulations where you can change inputs and observe results (similar to Cell Collective, but with more focus on the molecules themselves), but there are not many workbooks for cell biology. They have pdf instructions, so it may take some effort to figure out how you want students to submit their answers or integrate it with your LMS. There are a lot of good workbooks for Evolutionary topics if you want to incorporate that into your lessons.

Limitations: This is not free, and you need to install software. Some of the upper level tutorial labs seemed a little basic and not all of them were very “lab like,” although that may not be a bad thing depending on what you want students to accomplish. You can’t really customize it, and it actually seems to me like these would be more in line with reinforcing a lecture topic than providing the sort of experimentation and data analysis type of thing you might want in a lab. If I were to use this, I would probably consider it more as an activity for a flipped classroom type of endeavor as versus a full replacement for a lab. If you already use some type of interactive homework software like Pearson’s Mastering Biology, then it might be a little redundant.

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FoldIt

Category: Modeling & Simulation

Level: Fr/So/Jr/Sr

Duration: One lab period up to semester long participation.

Vision & Change: Structure Function

Synopsis:

This is a game interface that illustrates the concepts underlying protein folding by giving the user folding problems to solve. The puzzles are based on real data, and the idea is that predictive algorithms can only get you so far in modeling higher order protein structure, and a human touch can help solve the final piece of the puzzle. While the users “play” they not only contribute to real discoveries, but learn more about sidechain biochemistry, hydrogen bonding, hydrophobic interactions, steric hindrance, and more.

Strengths: The game-like interface, the ability to add a competitive spin to assignments if desired. You can work up to puzzles on real solutions like protein binding in the COVID inflammatory response and other drug design challenges. There is a dedicated Discord for it, so that might add a fun social element if you plan to have students get very involved with it.

Limitations: There are some teaching resources, but student accomplishments are not easily trackable by instructors. Not easily customizable, but you can create contests where students work on the same or related puzzles. You need to download software.

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Allen Integrated Cell

Category: Modeling & Simulation

Level: Fr/So/Jr/Sr

Duration: One lab period up

Vision & Change: Structure Function, Systems

Synopsis:

The Allen Integrated Cell site has a collection of microscopy images and videos that were taken from cell lines expressing fluorescently tagged proteins that highlight specific organelles/components. There are resources were you can look at 3D cells, rotate them around, and toggle certain aspects on and off. You can also look at digital reconstruction of cells during different phases of the cell cycle. Features I explored include the 3D Cell Explorer, the Cell Feature explorer, the Modeling and Analysis Resources, and the Visual Guide to Human Cells (under Animated Cell).

Strengths: In addition to a lot of really beautiful images that capture organelle dynamics, this site features instructor resources that could be used as virtual labs aimed at teaching neuroanatomy and mitosis. Instructors could also develop their own activities, such as having students browse differentiated cells and comparing things like actin/myosin content in muscle vs other cell types, for example. It has some integration with Jupyter Notebooks for those who are already comfortable with informatics approaches.

Limitations: Finding things on the site seems like it is a little confusing, especially if students aren’t yet familiar with the idea of numbered cell lines and protein tags. Aside from the published lab, there are not many educational resources, so it may take some work and creativity to use this for longer labs. This also focuses only on human iPSC cell lines, so there aren’t any resources for plant cells, or anything to illustrate the real diversity of cells that may have different organelles. There is some representation for differentiated cells and some CRISPR edited cells, but I don’t think they currently have resources for cells with disease mutations.

References:

Shelden, E. A., Offerdahl, E. G., & Johnson, G. T. (2019). A Virtual Laboratory on Cell Division Using a Publicly-Available Image Database. CourseSource. https://doi.org/10.24918/cs.2019.15

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NetLogo Web

Category: Modeling and Simulations

Level: Various

Duration: Various

Vision and Change

  • Core concepts emphasized: Systems
  • Core competencies emphasized: Modeling and simulation

Synopsis: NetLogo has an extensive library of sample models that can run in a browser. Models of DNA protein synthesis, diffusion, membrane formation or tumor growth are a few models that may be relevant to the cell biology group.

Strengths:

The interface is straightforward and the code as well as instructions for use are documented for each model. These models could be incorporated into a course for either synchronous or asynchronous application. NetLogo can also be used to create models making this a powerful tool.

Limitations:

The interface would complement a laboratory activity but it was not clear to me that it would be used in a standalone fashion for a remote laboratory experience.

Reference:

NetLogo itself: Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.

HubNet: Wilensky, U. & Stroup, W., 1999. HubNet. http://ccl.northwestern.edu/netlogo/hubnet.html. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.

http://www.netlogoweb.org/docs/faq

http://ccl.northwestern.edu/netlogo/faq.html

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LabXchange

Category: Modeling and simulations

Level: All levels

Duration: Varies

Vision & Change: Information flow, exchange and storage

Synopsis: This is a free online browser-based platform from Harvard that allows the instructor to create a class similar to learning management systems (i.e. canvas, moodle, blackboard) and search for content in various disciplines-biological sciences, chemistry, scientific process, and many more. This content comes in the form of pathways, interactives, simulations, case studies, assignment and much more.

Strengths: Very easy to use, customizable, permits tracking of student performance, includes a science and society section, and lots of simulations and other great content that could supplement and/or serve as great substitutes for some lessons and labs. Some of the cluster content also contains learning objectives and review assignments on the information presented.

Limitations: Labs seem to be designed to reinforce content covered in lectures.

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pClone: Synthetic Biology Tool

Category: Data Analysis and Presentation

Level: Introductory and Sophomore

Duration: Various

Vision and Change

  • Core concepts emphasized: Information Flow, Exchange and Storage
  • Core competencies emphasized: Process of Science

Synopsis: The experience is designed to study promoter regulation.

Strengths:

This experience has been designed, implemented and assessed by our colleagues at Davidson College. Students gain experience in molecular biology, cloning and analyzing phenotypes through quantitative methods. The experience has a strong emphasis on experimental design and analysis. Extensive support materials are provided in the supplementary information.

https://www.lifescied.org/doi/suppl/10.1187/cbe.13-09-0189/suppl_file/combinedsupmats1.pdf

Dr. Campbell has transitioned the laboratory experience into a remote laboratory. Here is a link to his course website with the week to week plan for students.

https://www.bio.davidson.edu/people/macampbell/113/2iterationsGGAstudent.html

Limitations:

Designed as an in-person, hands-on experience.

Reference:

Campbell AM, Eckdahl T, Cronk B, et al. pClone: Synthetic Biology Tool Makes Promoter Research Accessible to Beginning Biology Students. CBE Life Sci Educ. 2014;13(2):285-296. doi:10.1187/cbe.13-09-0189

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Western Blot Module

Category: Data Analysis

Level: Upper division

Duration: Three 3-hour lab periods plus 1hr of lecture

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: This project is designed to introduce students to the analysis of western blot data.

Strengths: This experience has been designed by our colleagues at the University of the South. Students gain experience analyzing raw western blot data. The experience is designed to replicate the analysis and writing aspect of conducting a scientific experiment. A few pages from a lab notebook are provided and students can be asked to analyze the data, write figures/legends, methods, results, etc. Materials are available here.

Limitations: Students don’t design the experiment. This module strongly emphasizes data analysis.

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Reporter Constructs Module

Category: Data Analysis

Level: Upper division

Duration: Three 3-hour lab periods plus 1hr of lecture

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: This project is designed to introduce students to the analysis of heat shock gene expression in C. elegans with reporter genes.

Strengths: This experience has been designed by our colleagues at the University of the South. Students gain experience analyzing raw fluorescence micrograph data of C. elegans exposed to heat shock. The experience is designed to replicate the analysis and writing aspect of conducting a scientific experiment. A few pages from a lab notebook are provided and students can be asked to analyze the data, write figures/legends, methods, results, etc. Materials are available here.

Limitations: Students don’t design the experiment. This module strongly emphasizes data analysis.

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qRT-PCR Module

Category: Data Analysis

Level: Upper division

Duration: Three 3-hour lab periods plus 1hr of lecture

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: This project is designed to introduce students to the analysis of heat shock gene expression in C. elegans by qRT-PCR

Strengths: This experience has been designed by our colleagues at the University of the South. Students gain experience analyzing raw qRT-PCR data. The experience is designed to replicate the analysis and writing aspect of conducting a scientific experiment. A few pages from a lab notebook are provided and students can be asked to analyze the data, write figures/legends, methods, results, etc. Materials are available here.

Limitations: Students don’t design the experiment. This module strongly emphasizes data analysis.

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Genotyping Module

Category: Data Analysis

Level: Upper division

Duration: Three 3-hour lab periods plus 1hr of lecture

Vision and Change

  • Core concepts emphasized: Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: This project is designed to introduce students to the analysis of C. elegans genotyping data.

Strengths: This experience has been designed by our colleagues at the University of the South. Students gain experience analyzing raw PCR-based genotyping data. The experience is designed to replicate the analysis and writing aspect of conducting a scientific experiment. A few pages from a lab notebook are provided and students can be asked to analyze the data, write figures/legends, methods, results, etc. Materials are available here.

Limitations: Students don’t design the experiment. This module strongly emphasizes data analysis.

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Virtual Microscope – FSU

Category: Data Analysis

Level: Variable

Duration: Variable

Vision and Change

  • Core concepts emphasized: Structure and Function
  • Core competencies emphasized: Process of Science

Synopsis: A wide variety of microscope techniques are available to preview specimens. Operators can control various features of the microscope.

Strengths: The user is able to explore different modes of microscope operation and view different samples. There is an opportunity to change acquisition parameters and become acquainted with the strengths and weaknesses for different types of microscopy. This would complement a microscope-based laboratory perhaps as a pre-lab activity.

Limitations: This is a demonstration of microscopy techniques so it does not permit student-generated investigation.

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Virtual Microscope – Beckman Imaging Group

Category: Data Analysis

Level: Variable

Duration: Variable

Vision and Change

  • Core concepts emphasized: Structure and Function
  • Core competencies emphasized: Process of Science

Synopsis: A virtual microscope emphasizes scanning electron microscopy and light microscopy.

Strengths: There are robust image datasets to explore

Limitations: The virtual microscope interface needs to be downloaded and may not run on newer systems. The interface must be downloaded to a computer and does not run directly on the web.

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3. Other Resources

This section contains helpful resources that are not ready-to-deploy modules per se. Items in this section include:

  • Repositories: Across the undergraduate STEM education community, many groups have collected and/or curated useful resources. To avoid excessive duplication of effort, the ACS Cell Biology Lab Working Group elected to reference these existing repositories rather than summarizing each item in each existing repository.
  • Software: Training the next generation of scientists will require that they become proficient at working with programs that aid in data analysis. Here we have highlighted some data analysis tools that are particularly appropriate for undergraduates and could be effectively leveraged in the context of a virtual or hybrid laboratory experience.

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CUREnet

Category: CURE

Level: Varies

Duration: Varies

Vision and Change:

  • Core concepts emphasized: Structure-Function, Information Flow
  • Core competencies emphasized: Process of Science

Synopsis: A collection of CUREs that have been provided by community members that are freely available for others to adopt and modify accordingly.

Strengths: allows different levels and types of research, provides student and research goals, design, instructional materials, assessments, and other helpful resources that gives it a “plug and play” feel.

Limitations: Although the website says that the projects listed are for all levels, most of them seem a little advanced for freshmen.

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Network for Integrating Bioinformatics into Life Sciences Education

Category: Resource Repository

Level: Varies

Duration: Varies

Vision and Change

  • Core concepts emphasized: Varies
  • Core competencies emphasized: Varies

Synopsis: This NSF-funded Research Coordination Network for Undergraduate Biology Education (RCN-UBE) curates bioinformatics teaching materials, including many published in CourseSource and CBE-Life Sciences Education, some of which are also featured later in this document. They also post teaching materials incubated in the NIBLSE consortium.

Strengths: Diverse resources with searchable tags. Some of the projects curated on this site can be adapted to the specific needs of a course or institution.

Limitations: At the time this document was crafted, only 32 projects were curated on the NIBLSE site.

References:

Dinsdale, E., Elgin, S. C., Grandgenett, N., Morgan, W., Rosenwald, A., Tapprich, W., … & Pauley, M. A. (2015). NIBLSE: A network for integrating bioinformatics into life sciences education. CBE—Life Sciences Education, 14(4), le3. https://doi.org/10.1187/cbe.15-06-0123

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Image J/Fiji

Category: Data Analysis and Presentation

Level: Any level

Duration: Variable

Vision and Change

  • Core concepts emphasized: Structure and Function
  • Core competencies emphasized: Process of Science

Synopsis: Image J is a powerful, open-source software package used for cell biology. Numerous opportunities to quantify images for data analysis activities. Extensive tutorials and documentation exist and the image.sc forum taps into the community of science when questions arise.

https://forum.image.sc/

Strengths: Research grade software with numerous add-on features for advanced image analysis. The community that develops resources for Image J is supportive. Here is a link for tutorials and examples:

https://imagej.nih.gov/ij/docs/examples/index.html

Limitations: Tutorials, lessons and experience necessary to become a proficient user. Acquisition of image data may require planning or collaboration.

Reference:

Schindelin, J., Arganda-Carreras, I., Frise, E. et al. Fiji: an open-source platform for biological-image analysis. Nat Methods 9, 676–682 (2012).

https://doi.org/10.1038/nmeth.2019

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Library of Integrated Network-based Cellular Signature

Category: Data Analysis and Presentation

Level: Upper-level

Duration: Variable

Vision and Change

  • Core concepts emphasized: Structure and Function
  • Core competencies emphasized: Process of Science

Synopsis: The database is associated with the Open Microscopy Environment (OME). Publicly available data available that is linked to publications.

Strengths: This might make for a good exercise for permitting students to analyze or present data and comparing their results to the author’s findings. There is an abundant amount of data available and it is searchable by cell line, antibody etc.

Limitations: A fair amount of planning and exploration would be required to prepare an investigation for students.

Reference:

Sansone, S., Rocca-Serra, P., Field, D. et al. Toward interoperable bioscience data. Nat Genet 44, 121–126 (2012).

https://doi.org/10.1038/ng.1054

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DEBrowser for R/RStudio

Category: Bioinformatics and Data Analytics

Level: Fr/So/Jr/Sr

Duration: One lab period up to semester long discovery

Vision & Change: Systems, Information Flow

Synopsis: DEBrowser is a package in R that allows you to visualize differential expression from RNAseq data from NCBI. You can see how gene expression changes in different conditions, tissues, or developmental stages.

Strengths: Even though it’s in R, no coding skills are required beyond installing and initializing DEBrowser because once you run it, it launches a GUI that is very intuitive to use. It can be used on its own or paired with literature reviews on the front end to find RNAseq data and potential characterization of genes that are identified in the process. It can also be used in conjunction with other informatics resources (especially the DNA subway green line) for a tailored, CURE-like experience.

Limitations: Requires uploading a sample information and metadata file, which has to be created for the analysis you’re using. This can be done ahead of time by the instructor, or advanced students can make their own. There are not that many example files available. The interface is intuitive but there aren’t many resources that are ready out of the box for instructors. As of now it requires R to be installed on a desktop computer or you’ll need to use a virtual environment with everything installed. Assessments cannot be tracked directly, but can be manually created in a LMS.

References:

Kucukural A, Yukselen O, Ozata DM, Moore MJ, Garber M (2019). “DEBrowser: Interactive Differential Expression Analysis and Visualization Tool for Count Data.” BMC Genomics, 20, 6. doi: 10.1186/s12864-018-5362x, https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-5362-x.

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DNA Subway

Category: Bioinformatics and Data Analytics

Level: Fr/So/Jr/Sr

Duration: One lab period up to semester long discovery

Vision & Change: Information Flow, Evolution

Synopsis: DNA Subway is a browser-based pipeline for gene annotation and genome/transcriptome analysis that allows users to do a wide variety of analyses within a single user interface. It has different “lines” that represent different types of data analysis like looking through a sequence for repeats and mapping them back to transposons.

YouTube: https://youtu.be/k9lKxp3ITY0

Strengths: Resources have already been developed specifically for CUREs, and it is designed for education. It is a browser-based applications that doesn’t require downloads. You can give students sequences/files ahead of time to use in class or have them investigate sequences of their own choosing. There are sample sequences available in the program to use. Provides a step-by-step, in depth look at a genome using common databases and tools so students can get experience with important concepts that they will need later.

Limitations: Not every step is intuitive if students are not already familiar with what results from a database might look like.

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iBiology: Tips for Science Trainees Videos

Category: Data Analysis and Presentation

Level: Variable

Duration: Variable

Vision and Change

  • Core concept emphasized: Variable
  • Core competencies emphasized: Process of Science, Ability to Communicate

Synopsis: A series of short videos addressing a wide range of topics relevant to science trainees. Topics related to science writing and presentation are included.

Strengths: These videos may be valuable for students who are intending on pursuing a career in science. Additionally, specific video clips may address a particular area of interest an instructor is focusing on with their class.

Limitations: These resources are not specifically designed for an undergraduate student audience.

Reference: N/A

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