The last decade has witnessed dramatic changes in the field of genomics with the advent of high-throughput DNA sequencing technologies. Sequencers have become the ultimate tool for a wide range of applications, from prenatal genetic screens and microbe identification to forensic sciences and autopsies. As such, genomics requires interdisciplinary thinking that involves concepts from molecular biology, statistics, computer science, and ethical and societal issues. Previous work has highlighted the benefit of hands-on training to help students put these concepts into context (Altman 1998; Donovan, 2008; Reisdorph et al., 2013; Magana et al., 2014). Hands-on training is also the preferred learning style of the Millennial generation, which currently makes up the majority of undergraduate and graduate students. Research has shown that people in this generation are technology focused, work most effectively in groups, and absorb information most efficiently by kinesthetic learning (learning by doing; Shapiro et al., 2013; Evans et al., 2015; Linderman et al., 2015).

Here, we describe our experience of using mobile DNA sequencers in the classroom to facilitate hands-on learning. Our class focused on the newest sequencing technology: the MinION by Oxford Nanopore Technologies (ONT). Unlike other sequencing technologies that are static and require a laboratory setting, the MinION sequencer is slightly larger than a typical USB stick and only requires a laptop to run (Figure 1A and B; Gardy et al., 2015; McIntyre et al., 2015; Erlich, 2015).

Figure 1

Download asset Open asset

We developed a course for Columbia University entitled ‘Ubiquitous Genomics’ that brings portable sequencing to the classroom. The Computer Science department offered the course as an elective. Of the 20 students who enrolled in the course, 50% were studying towards a bachelor’s, 30% towards a master’s degree, and 20% were enrolled in a PhD program. The majority (~60%) of students were enrolled in a computer science program, and the rest were enrolled in other programs, including electrical engineering, environmental health science and biomedical informatics. The class had no prerequisites, but nearly all students had some programming experience and about a third of the students had taken at least one class in computational biology. Students with computational biology experience performed slightly better in our class.

The course consisted of 13 meetings (one two-hour class per week) and was separated into a theoretical section and an applied section (Supplementary file 1). The theoretical section overviewed sequencing technologies and their potential uses in medicine, bio-surveillance, forensics, and ethical aspects of DNA sequencing, such as genetic privacy and the ability of participants to comprehend risks and potential harm. The aim of the theoretical section was to create a common ground for the group of students with diverse majors and background knowledge. The format was an interactive seminar where the class discussed one or two recent research papers.

The applied section comprised two three-week blocks of “hackathons” that included MinION sequencing, data analysis and an assignment. We estimate the consumable costs of a hackathon to be on the order of $1,000 per team per assignment (Table 1). However, nearly 90% of the cost is due to the MinION sequencer and any reduction in its price will affect the projection of the costs. We decided to use the term hackathon to convey to the students that, unlike a regular course lab, the questions were open-ended and even we (the instructors) did not always know the answers or the best tools to solve the assignments. In the first hackathon, entitled “from snack to sequence”, the students received unlabeled DNA collected from food and supermarket ingredients. They had to use the sequencers to collect the DNA data and devise a pipeline to infer the ingredients. In the second hackathon, called “CSI Columbia”, the students sequenced several human DNA samples without knowing the identity of the samples. The hackathon focused on collecting data from these samples and students were encouraged to try any possible method they could imagine to generate investigative leads.

To address our teaching goals, we set the three week hackathon cycle as follows: in the first week of each hackathon-block, the students met for a ~3-hour session, in which they worked in groups to set up the MinION sequencer, generate data, and start strategizing about the best approach to answer the assignment. In the second week, we had a meeting with the students to discuss technical issues related to the assignment, such as the best approach to identify an organism from MinION data. Each group had to explore a different approach and to report the results in a 5-minute presentation to the rest of the class. In the final class of each hackathon-block, the students presented their results and turned in their written assignments (Supplementary file 1).

Naturally, the most challenging classes to prepare for were the MinION sessions. We employed several strategies to maximize the hands-on experience of the students within the time constraints of the class (Figure 2):

Figure 2

Download asset Open asset

A week before the hackathon, the students were instructed to form groups of 4–5 people. We encouraged them to form groups with diverse skill sets (e.g. combinations of biology backgrounds and computer science backgrounds).

Several days before the hackathons, the instructors prepared the DNA libraries for the class. We decided to do this part ourselves and not as part of the training, since genomic DNA extraction and ONT library preparation takes ~4 hr (Supplementary file 2). It was not realistic to include these steps as part of the hackathon given the time limits (although this might change with the advent of the automated library preparation device, the VolTRAX).

Each hackathon started by tuning student expectations; we reminded the students about the experimental nature of the events. We communicated clearly that they should anticipate technical issues and that we would be surprised if everything went smoothly. This helped to reduce frustration for the students, who are accustomed to interacting with mature technology in day-to-day life. We continued with a 45-minute lecture about the goals of the hackathon and background material, such as how the DNA libraries were prepared, the MinION software interface features, and the base-calling pipeline (see Supplementary files 3–6 for assignments and PowerPoint slides).

Next, we had students practice pipetting. The loading of reagents onto the MinION flow cell requires good pipetting skills; otherwise, the yield may be substantially lower. As most of our students had never touched a pipette before, we allowed them to practice loading water onto used MinION flow cells until they were comfortable pipetting with precision.

Armed with a protocol, the students were fully responsible for generating the data with minimal assistance. They connected the devices to the computers, activated the relevant programs, loaded the priming mix (dubbed ‘fuel’) and DNA libraries onto the flow cells, and launched the sequencing run using MinKnow. Once data was generated, they monitored the progress of the sequencing run. After checking quality measures, the sequencers were left unattended for 48 hours to generate data according to the ONT protocol.

After data generation, we instructed the students to complete an assignment, which was divided into two milestones (Supplementary files 5 and 6). The first milestone was to report on the technical performance of the MinION sequencer, such as the total reads, the read length distribution, DNA library quality, and the read quality scores over time. The aim of the quality control analysis was to guide the students on how to approach large genomic data sets. The second milestone focused on an actual scientific problem (see below). For each milestone, the students had to submit a written report and a GitHub link to their code (an example: https://github.com/dspeyer/ubiq_genome). Each hackathon concluded with a 10-minute talk by each group. All relevant teaching material is provided under the Creative Commons Attribution-Share Alike 4.0 International License.

The first hackathon was called “from snack to sequence”. It was inspired by several food scandals, such as the horsemeat found in ready-made meals that were labeled as beef throughout Europe in 2013, as well as the revelation that a number of sushi restaurants in New York City claimed to be selling white tuna but in reality were serving escolar. Based on this issue, we wanted to introduce students to the identification of species in different food items.

We prepared five sequencing libraries from dishes purchased at local restaurants and raw food products that were purchased at a supermarket. The DNA libraries were a mix of multiple ingredients (like raw beef and tomato). We set out to address the following questions with the students: a) Can you identify the species in a food sample using MinION sequencing, without prior knowledge? b) Can you quantify the composition of the different ingredients? c) What is the minimal sequencing runtime required to detect the ingredients of the sample?

After generating the data in the hackathons, we devoted the next class to exploring a diverse number of sequencing algorithms that could be used for species identification. Importantly, Oxford Nanopore’s ‘What’s In My Pot’ species identification workflow does not support the identification of eukaryotic samples (Juul et al., 2015) and the students had to find alternatives. The consensus among the students of the class was that a Basic Local Alignment Search Tool (BLAST) was the best option.

Most groups were able to identify the species within the dish. One interesting discussion resulted from the two groups that sequenced samples putatively containing beef. The top BLAST hit was for bighorn sheep (Ovis canadensis), whereas the domesticated sheep (Ovis aries) or cow (Bos taurus) was returned with lower alignment quality values. The identification of bighorn sheep was suspicious, since this animal is not domesticated. Cow is part of the Bovidae family, as are the bighorn and domesticated sheep. The students reasoned that the sample could be from a family member and selected the domesticated sheep as the most likely candidate. A surprising finding was the detection of DNA from the parasites Babesia bigemina, Wuchereria bancrofti and Onchocerca ochengi in the raw beef samples (at least two or more reads per parasite). These findings led to a vivid discussion in the class on food safety. (Note: After reading a previous version of this manuscript on bioRxiv, Steven Salzberg noted that the Genbank sequences of these parasites are likely to be contaminated with cow DNA. Thus, the BLAST matches to these parasites do not conclusively indicate that they were present in the food samples.)

Overall, this hackathon was academically apt for the level of the students. The only technical challenge the students repeatedly encountered was how to BLAST a large number of query sequences using the application programming interface (API). They had to find creative solutions, such as mirroring the National Institutes of Health (NIH) BLAST to a private server and tweaking the input parameters to make it possible to search a large number of long MinION reads.

For the second hackathon, we explored the identification of individuals using ultra low coverage genome sequencing with the MinION. In forensics, DNA evidence identification relies on the analysis of the 13 well-characterized Combined DNA Index System (CODIS) short tandem repeat (STR) loci (Kayser and de Knijff 2011). However, theoretical analysis has suggested that a small number (30–80) of common single nucleotide polymorphisms that are inherited independently of each other are sufficient for positive identification (Lin et al., 2004). The aim of this hackathon was to test whether it would possible to use this technique to identify individuals using MinION shotgun sequencing with extremely shallow coverage. We also encouraged the students to test various methods to identify the person, such as examining the mitochondrial haplogroup, the sex of the person, and estimating his or her ancestry. In any case, our expectations were focused on their scientific decision process rather than the answer, and the students were encouraged to send the instructors questions when they required help.

Two groups sequenced a DNA library prepared from genomic DNA from Craig Venter (Levy et al., 2007), one group sequenced a HapMap sample from the 1000 Genomes Project, and two groups sequenced the genomic DNA of one of the authors (YE). We chose these individuals because of their publically available DNA reference data. The students initially did not know the identity of the sequenced genome, but in a later stage of the hackathon we told them that their sample was either one of the following individuals: Craig Venter, Jim Watson, the author (YE), or a participant of the 1000 Genomes Project.

The students found this assignment much more challenging than the previous one. Of the five groups, one was able to correctly identify their input sample (Craig Venter). The students tried an impressive array of tools but their main challenge was data wrangling. They had to convert their data to various formats in order to test different tools just to realize that the tools did not perform as expected or were poorly documented, wasting a significant amount of time. Interestingly, some of the undergraduate students told us later that this was the first time they were exposed to an open-ended real-world research problem and that this task gave them a better understanding of academic research. The students also suggested that more discussions between the groups during the hackathon could have helped to solve some of the technical problems. This can be done using online communication tools (like Facebook or a Piazza website). Future instructors of this hackathon can circumvent some of the difficulties by restricting the scope of the analysis. For example, instead of instructing the students to generate any possible identity lead, students could be told to focus only on ancestry analysis from shotgun sequencing or sequence specific regions such as the mitochondria for a more structured analysis.

Prepare spare parts: We experienced multiple technical difficulties in the 10 intended MinION runs (five groups over in two hackathons). Three flow cells had an insufficient pore number (<51) and had to be replaced. In another event, a computer failed to connect with any MinION instruments despite a working USB 3.0 port. During the hackathon, there is little time to troubleshoot. It is therefore crucial to anticipate scenarios of failure and have spare parts (i.e. computers, flow cells, fuel mix, and DNA library).

Consider back-up data: As part of testing our hackathon setting, we sequenced some of the DNA libraries with the MinION before the actual event. The data generated from these tests was kept to have a contingency plan in case none of the MinIONs worked at the time of the hackathon. This way students would still have data to analyze, and the course progression would not be jeopardized. While we fortunately did not have to use this data, we encourage MinION hackathon organizers to consider this option.

Expect variability in the amount of data: The yield of the MinION sequencers varied between runs. The experimental design and the questions posed during each hackathon should be compatible with both a low and a high sequencing yield.

Locate appropriate computers: One of our main challenges was to procure five computers that matched ONT specifications. Our department is almost entirely Mac-based, whereas the current ONT specification requires a Microsoft Windows computer. We tried installing Windows virtual machines on our Macintosh computers but found this solution unreliable, presumably due to the fast data transmission rates of the sequencers. The students’ computers also fell short of the specifications required by ONT, such as having a solid-state drive. MinION hackathon organizers should keep in mind that locating multiple appropriate computers can be a time-demanding task.

Network: ONT sequencing requires an Internet connection for base-calling. We connected the five computers to a regular network hub using a standard Ethernet protocol. We did not experience any issues.

Use free tools for data transfer: MinION sequencing can result in large data folders. We looked for a free program to automatically transfer the data 48 hrs after the start of the run from the sequencing laptop to the students’ computers. Cloud-based products, such as Dropbox, do not support synchronizing this amount of data with their free accounts. As an alternative, we used the free version of BitTorrent Sync, which allows sharing of files over the P2P BitTorrrent network without a size limit. BitTorrent can be pre-installed on the workstation and can be synchronized with the student’s personal computer by exchanging a folder-specific key. This solution for large files can be set up within a few minutes and prevents technical challenges.

We sought to learn more quantitatively about the views of students with respect to genomics and mobile sequencing. We asked them to answer a questionnaire before the first hackathon, when the students were exposed only to the theory of sequencing and its applications, and then three weeks later, after the completion of the first hackathon.

While our sample size is too small to draw statistical conclusions, we did learn from the trends in the answers. The hackathons seemed to have shaped a more realistic view of the technical challenges inherent to genomic applications. For instance, for the question “How long do you think it takes from sample preparation to sequencing results using MinION?”, about 70% of the students answered ‘one hour’ (or less) before the hackathon; but after the hackathon, only 30% of the students thought it would take one hour. After the hackathons, students also thought that it would take more time for mobile sequencers to be used for health tracking by the general public and suggested lower costs for home sequencing applications. Despite discussing the ethical implications of DNA analysis quite extensively throughout the course, we did not observe changes in the students views on several ethical issues such as “Do you think it is ethical to sequence hair found on the street?” or “Do you think getting your genome sequenced is safe?” These trends suggest that the hackathon mainly shaped the students’ technical understanding and demonstrated the value of hands-on experience as a way of helping them to develop realistic views of the challenges of new technologies.

Mobile sequencing in the classroom proved to be a useful method for teaching students about the cross-disciplinary field of genomics and to contextualize genomic concepts. These devices are relatively inexpensive and do not require complicated equipment or designated lab space to be operated. As such, they dramatically reduce the barrier to classroom integration compared to other sequencing technologies.

The main focus of this manuscript was the use of mobile sequencing in the higher education system (undergraduate and postgraduate). Even though most students were computer science majors, it could be suitable for other majors such as molecular biology and pharmacy, and also for medical school students. We highly recommend instructors of students with limited programming backgrounds to design assignments that use existing data analysis pipelines (such as ONT’s “What’s in my pot” tool). It might also be useful to customize the assignments to the major of the students. For example, for biology students, the assignment could focus on taxonomy, while medical students could benefit from sequencing microbes that are known to cause disease. We also see the potential of using these devices in high school STEM curricula and enrichment programs. Such activities can expose pupils early in their training to the fascinating world of DNA and serve as an educational springboard to study other disciplines such as math, computer science and chemistry. We hope that the resources and experience outlined in this manuscript will help to facilitate the advent of these programs.

1. 1. Altman RB

   (1998) A curriculum for bioinformatics: The time is ripe

   Bioinformatics 14:549–550.

   https://doi.org/10.1093/bioinformatics/14.7.549

   • Google Scholar
2. 1. Donovan S

   (2008) Big data: Teaching must evolve to keep up with advances

   Nature 455:461.

   https://doi.org/10.1038/455461d

   • Google Scholar
3. 1. Erlich Y

   (2015) A vision for ubiquitous sequencing

   Genome Research 25:1411–1416.

   https://doi.org/10.1101/gr.191692.115

   • Google Scholar
4. 1. Evans KH
   2. Ozdalga E
   3. Ahuja N

   (2016) The medical education of generation Y

   Academic Psychiatry 40:382–385.

   https://doi.org/10.1007/s40596-015-0399-5

   • Google Scholar
5. 1. Gardy J
   2. Loman NJ
   3. Rambaut A

   (2015) Real-time digital pathogen surveillance — the time is now

   Genome Biology 16:155.

   https://doi.org/10.1186/s13059-015-0726-x

   • Google Scholar
6. 1. Juul S
   2. Izquierdo F
   3. Hurst A
   4. Dai X
   5. Wright A
   6. Kulesha E
   7. Pettett R
   8. Turner DJ

   (2015) What’s in my pot? Real-time species identification on the minion

   bioRxiv.

   https://doi.org/10.1101/030742

   • Google Scholar
7. 1. Kayser M
   2. de Knijff P

   (2011) Improving human forensics through advances in genetics, genomics and molecular biology

   Nature Reviews Genetics 12:179–192.

   https://doi.org/10.1038/nrg2952

   • Google Scholar
8. 1. Levy S
   2. Sutton G
   3. Ng PC
   4. Feuk L
   5. Halpern AL
   6. Walenz BP
   7. Axelrod N
   8. Huang J
   9. Kirkness EF
   10. Denisov G
   11. Lin Y
   12. MacDonald JR
   13. Pang AW
   14. Shago M
   15. Stockwell TB
   16. Tsiamouri A
   17. Bafna V
   18. Bansal V
   19. Kravitz SA
   20. Busam DA
   21. Beeson KY
   22. McIntosh TC
   23. Remington KA
   24. Abril JF
   25. Gill J
   26. Borman J
   27. Rogers YH
   28. Frazier ME
   29. Scherer SW
   30. Strausberg RL
   31. Venter JC

   (2007) The diploid genome sequence of an individual human

   PLoS Biology 5:e254.

   https://doi.org/10.1371/journal.pbio.0050254

   • Google Scholar
9. 1. Lin Z
   2. Owen AB
   3. Altman RB

   (2004) Genetics. genomic research and human subject privacy

   Science 305:183.

   https://doi.org/10.1126/science.1095019

   • Google Scholar
10. 1. Linderman MD
    2. Bashir A
    3. Diaz GA
    4. Kasarskis A
    5. Sanderson SC
    6. Zinberg RE
    7. Mahajan M
    8. Shah H
    9. Suckiel S
    10. Zweig M
    11. Schadt EE

    (2015) Preparing the next generation of genomicists: A laboratory-style course in medical genomics

    BMC Medical Genomics 8:47.

    https://doi.org/10.1186/s12920-015-0124-y

    • Google Scholar
11. 1. Magana AJ
    2. Taleyarkhan M
    3. Alvarado DR
    4. Kane M
    5. Springer J
    6. Clase K

    (2014) A survey of scholarly literature describing the field of bioinformatics education and bioinformatics educational research

    CBE Life Sciences Education 13:607–623.

    https://doi.org/10.1187/cbe.13-10-0193

    • Google Scholar
12. 1. McIntyre ABR
    2. Rizzardi L
    3. Yu AM
    4. Rosen GL
    5. Alexander N
    6. Botkin DJ
    7. John KK

    (2015) Nanopore sequencing in microgravity

    BioRxiv.

    https://doi.org/10.1101/032342

    • Google Scholar
13. 1. Reisdorph N
    2. Stearman R
    3. Kechris K
    4. Phang TL
    5. Reisdorph R
    6. Prenni J
    7. Erle DJ
    8. Coldren C
    9. Schey K
    10. Nesvizhskii A
    11. Geraci M

    (2013) Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics

    Genomics, Proteomics & Bioinformatics 11:368.

    https://doi.org/10.1016/j.gpb.2013.10.002

    • Google Scholar
14. 1. Shapiro C
    2. Ayon C
    3. Moberg-Parker J
    4. Levis-Fitzgerald M
    5. Sanders ER

    (2013) Strategies for using peer-assisted learning effectively in an undergraduate bioinformatics course

    Biochemistry and Molecular Biology Education 41:24–33.

    https://doi.org/10.1002/bmb.20665

    • Google Scholar

Thank you for submitting your article "Integration of mobile sequencers in an academic classroom" to eLife for consideration as a Feature Article. Your article has been reviewed by Samuel Donovan, Michael Linderman, and Nicholas Loman, and the evaluation was overseen by a member of the eLife staff (Sarah Shailes, Assistant Features Editor).

The peer reviewers and Dr Shailes have drafted this decision letter to help you submit a revised version of the article.

Dr Shailes will also contact you separately about some editorial issues that you will need to address.

Summary:

This was an enjoyable and interesting report about a hands-on workshop to train students in evolution, genomics, sequencing and bioinformatics employing the MinION portable genome sequencer. For the course, students completed a six-week introductory block of seminar-style classes and then two three-week "hackathons" in which they performed experiments using the MiniION sequencer. The paper has the potential to raise awareness about, and provide a concrete example of, the types of authentic science that can be done in undergraduate classrooms. This is an area of intense interest in the science education community and many efforts are underway suggesting that there should be a broad audience interested in this report.

The report highlights several important design features that are worth sharing including the interdisciplinary nature of the course, the "hackathon" instructional framework, and the focus on data management skills. The reported exercises and "lessons learned" will be valuable to other educators considering running a similar course.

Before publication, the following comments should be addressed.

Essential revisions:

1) As it is currently organized, the manuscript focuses too narrowly on the details of the technology employed. The context of this course is important, but the broader potential for informing diverse educational settings is not as clear as it could be. Please, therefore, include a fuller discussion of the key features of the approach, for example:

1a) The audience. Was the course advertised as a "bioinformatics" course? Did it have any prerequisites? Did students without a bioinformatics background have any particular difficulties with the hackathon projects? One or two sentences about the framing of the course and the experiences of different kinds of students would help provide context and orient other educators considering similar techniques.

1b) Using a "hackathon" instructional model – share with the audience what the features of a hackathon are and how it structured the classroom.

1c) Tuning student expectations – this strikes me as essential when working on authentic tasks.

1d) At a certain level this curriculum could be criticized as a "canned lab" because of the ways that the samples were prepared, and the data collection is highly automated. The reviewers don't share this criticism but they think that it may be useful to articulate the learning goals in order to highlight all of the challenging work that was done even when the data generation did not involve a lot of student decisions.

As each of these points are introduced as challenges/opportunities you can follow-up with details from the course that demonstrate how those opportunities were leveraged for engagement and learning.

2) Please include more fine details of the methods used in the course to help other instructors to reproduce it themselves. It might be better to give an exhaustive "kit list" and perhaps even costs associated with setting up such a workshop.

3) Subsection “Questionnaire”: you report that "We did not observe changes before and after the hackathon for ethical issues…". How much discussion of potential ethical issues was included in the syllabus? That is, would any changes in such views (or not) result only from their personal experiences, or were students deliberately exposed to different viewpoints on those questions?

4) What are the ethical implications of using the instructors' own DNA samples in the CSI practical? A discussion on this point would be useful.

5) The CSI practical is much less likely to "succeed" than the food experiment because shallow coverage nanopore sequencing is quite error prone (10% error at best). There should be some discussion about making this practical more likely to succeed, either by doing something like mitochondrial sequencing or some bioinformatics method of imputation that is sensitive to nanopore error model.

Summary:

This was an enjoyable and interesting report about a hands-on workshop to train students in evolution, genomics, sequencing and bioinformatics employing the MinION portable genome sequencer. For the course, students completed a six-week introductory block of seminar-style classes and then two three-week "hackathons" in which they performed experiments using the MiniION sequencer. The paper has the potential to raise awareness about, and provide a concrete example of, the types of authentic science that can be done in undergraduate classrooms. This is an area of intense interest in the science education community and many efforts are underway suggesting that there should be a broad audience interested in this report. The report highlights several important design features that are worth sharing including the interdisciplinary nature of the course, the "hackathon" instructional framework, and the focus on data management skills. The reported exercises and "lessons learned" will be valuable to other educators considering running a similar course.

We thank the reviewers for sharing with us the excitement about integrating MinION sequencers in the classroom and for taking the time to carefully read and comment on our manuscript.

Before publication, the following comments should be addressed. Essential revisions: 1) As it is currently organized, the manuscript focuses too narrowly on the details of the technology employed. The context of this course is important, but the broader potential for informing diverse educational settings is not as clear as it could be. Please, therefore, include a fuller discussion of the key features of the approach, for example: 1a) The audience. Was the course advertised as a "bioinformatics" course? Did it have any prerequisites? Did students without a bioinformatics background have any particular difficulties with the hackathon projects? One or two sentences about the framing of the course and the experiences of different kinds of students would help provide context and orient other educators considering similar techniques.

We thank the reviewers for pointing this out. We added to the opening paragraph of the section “Overview of the Ubiquitous Genomics class” an in-depth description of the students’ background and the class. We also added to the conclusion section a short discussion on adaptation of the course to students from other disciplines.

1b) Using a "hackathon" instructional model – share with the audience what the features of a hackathon are and how it structured the classroom.

Great suggestion. The revised manuscript has a new section called “hackathon structure” that provides details on the hackathon sessions. The first paragraph highlights the differences from a regular frontal class.

1c) Tuning student expectations – this strikes me as essential when working on authentic tasks.

We thank the reviewers for this comment, we agree and therefore moved this from “lessons learned” to the hackathon progress.

1d) At a certain level this curriculum could be criticized as a "canned lab" because of the ways that the samples were prepared, and the data collection is highly automated. The reviewers don't share this criticism but they think that it may be useful to articulate the learning goals in order to highlight all of the challenging work that was done even when the data generation did not involve a lot of student decisions.

We respectfully disagree with description that the hackathons are canned labs. The assignments had multiple open-ended tasks and even we, the instructors, did not know what the best set of tools to approach the task were or what the results would be. For example, we did not envision finding traces of cattle parasites in raw meat or identifying beef as big sheep horn or the best strategy to identify a person from MinION data.

The revised text stresses this point. We also included the learning goals in Supplemental Note 1.

As each of these points are introduced as challenges/opportunities you can follow-up with details from the course that demonstrate how those opportunities were leveraged for engagement and learning. 2) Please include more fine details of the methods used in the course to help other instructors to reproduce it themselves. It might be better to give an exhaustive "kit list" and perhaps even costs associated with setting up such a workshop.

We thank the reviewers for this excellent point. Table 1 provides a list of reagents and their cost per reaction.

3) Subsection “Questionnaire”: you report that "We did not observe changes before and after the hackathon for ethical issues…". How much discussion of potential ethical issues was included in the syllabus? That is, would any changes in such views (or not) result only from their personal experiences, or were students deliberately exposed to different viewpoints on those questions?

We apologize for not making this point more clear. We covered ethical issues in Class #4, including genetic privacy, the US GINA legislation, and potential harm to participants. In addition, ethical questions were raised naturally in other contexts such as human identification and the meaning of race versus ethnic group. We revised the text (second paragraph subsection “Overview of the Ubiquitous Genomics class”) to highlight that ethics was part of the curriculum.

4) What are the ethical implications of using the instructors' own DNA samples in the CSI practical? A discussion on this point would be useful.

The DNA that was used belongs to Dr. Yaniv Erlich, the leading instructor of the class. One of the main topics of Dr. Erlich’s research is genetic privacy. He volunteered his DNA with full consent and awareness of privacy issues. Therefore, we do not see any difference between using his genome to the other samples (e.g. Venter).

Beyond personal interest, we used Dr. Erlich’s DNA because his genome is publicly available on the Internet. We added a sentence that this was our inclusion criterion for DNA samples and also added a link where the genome can be found.

5) The CSI practical is much less likely to "succeed" than the food experiment because shallow coverage nanopore sequencing is quite error prone (10% error at best). There should be some discussion about making this practical more likely to succeed, either by doing something like mitochondrial sequencing or some bioinformatics method of imputation that is sensitive to nanopore error model.

Excellent point. We added several sentences to the closing paragraph of the CSI Columbia section that describe how to vary the scope of the task to make it less challenging.

Author details

1. Sophie Zaaijer

   1. Department of Computer Science, Fu Foundation School of Engineering, Columbia University, New York, United States
   2. New York Genome Center, New York, United States

   Contribution

   SZ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Columbia University Ubiquitous Genomics 2015 class

   Competing interests

   The authors declare that no competing interests exist.

   1. Maya Anand
   2. Anubha Bhargava
   3. Anne Bozack
   4. Michael Curry
   5. Alexander Kalicki
   6. Xinyi Li
   7. Katie Lin
   8. Michael Nguyen
   9. Diego Paris
   10. Cheyenne Parsley
   11. Robert Piccone
   12. Garrett Roberts
   13. Daniel Speyer
   14. David Streid
   15. Brian Trippe
   16. Shashwat Vajpeyi
   17. Boyu Wang
   18. Lilly Wang
   19. Tia Zhao
   20. Liyuan Zhu

3. Yaniv Erlich

   1. Department of Computer Science, Fu Foundation School of Engineering, Columbia University, New York, United States
   2. New York Genome Center, New York, United States
   3. Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, United States

   Contribution

   YE, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   yaniv@cs.columbia.edu

   Competing interests

   The authors declare that no competing interests exist.

• 4,396

  views

• 1,055

  downloads

• 36

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.