Integrating commercially available industry-relevant software in the education of genome variant curation at the Masters level

Background/context. Genome analytics is a drastically expanding field, and there is a high demand for individualswith the necessary skillset to analyse the genome data that is being generated.A new Masters by coursework wasdeveloped to train individuals in genome analytics. Institutions analysinggenomes commonly utilise in-house analysis tools, but increasingly commercialsoftware packages that integrate AI are being considered in the research anddiagnostic space (De La Vega et al., 2021).

The initiative/practice. This led us to exposestudents to the use and limitations of commercial software packages during twocore units of the course, and evaluate changes in understandingof genome analytics, confidence in working in related fields and theirperspective of the integration of commercial software in graduate education.

Methods of evaluative data collectionand analysis. Studentswere invited to voluntarily complete an online survey including quantitative andqualitative components, both pre and post exposure to the software. Paired datafrom 23 individuals (73% response rate), most aged between 18 and 25, wererecorded and anonymised prior to analysis. Qualitative data were thematically coded blind by two individuals independentlyusing emergent coding (Charmaz, 2008).

Evidence of outcomes andeffectiveness. This project indicates that afterthe completion of the units that integrated commercially available industrysoftware, we measured increased student confidence (increase in percentage reportingfairly confident or higher) in joining the genetic analysis workforce(significant change from 37% to 70%) and incompleting job-specific tasks (significant increase in 7 out of 9 tasksof between 28% to 39%). The aspects of theirstudies the students valued in relation to these changes and their perceptionof the usefulness of integration of the commercial software were elucidatedfrom qualitative theming, and can inform others looking to integratecommercially available software within their tertiary degree.

References.

Charmaz, K. (2008).  Groundedtheory as an emergent method. In S. N. Hesse-Biber & P. Leavy (Eds.), Handbook of emergent methods. (pp.155-170). The Guilford Press.

De La Vega, F.M., Chowdhury, S., Moore, B.,Frise, E., McCarthy, J., Hernandez, E.J., Wong, T., James, K., Guidugli, L.,Agrawal, P.B., Genetti, C.A., Brownstein, C.A., Beggs, A.H., Löscher, B.S.,Franke, A., Boone, B., Levy, S.E., Õunap, K., Pajusalu, S., … Kingsmore, S.F.(2021). Artificial intelligence enables comprehensive genome interpretation andnomination of candidate diagnoses for rare genetic diseases. Genome Med, 13(1), 153. https://doi.org/10.1186/s13073-021-00965-0