Using user-centered design to unburden genetic analyses for novice genomic researchers

Abstract

Increasingly larger genomic databases have allowed for more robust genetic analyses,leading to advances in bioinformatics, translational medicine, and, ultimately, improving patient care. However, the current landscape of genetic analysis software is riddled with unintuitive and inaccessible tools and software packages. These tools often lack proper documentation, need extensive setup, fail to communicate with each other, and require painstaking debugging for even simple exploratory analyses. This creates large barriers of entry for novice genomic researchers (NGRs), individuals who are interested in conducting genetic experiments but either lack the computational experience/biological background or do not have access to extensive technological resources, such as local computational clusters. Historically, very little work has been done to address the needs of NGRs, leading to an overlooked, but keystone user base that lacks proper foundational support needed to best begin their informatics journey. User-centered design (UCD) is one solution to this problem that has been under-utilized in bioinformatics software development. In this work, we sought to better characterize the NGR user base and to apply the UCD framework during the development of a more usable bioinformatics software tool. To achieve this, we first explored the existing landscape of bioinformatics software tools via a literature review and sought to create a rubric that can be utilized to evaluate the usability of those tools within the context of NGRs. To further inform the creation of this rubric, we also performed a needs assessment of NGRs utilizing semi-structured interviews. From these two sources of knowledge, we found that the key attributes that resulted in poor adoption and sustained use of most bioinformatics tools included poor documentation, lack of context-specific instructional content, difficulty in installation and setup, and uninformative error messages (Aim 1). We then created user personas to help better characterize specific types of users and utilized those personas to help design a cloud-agnostic, user-friendly GWAS analysis tool (UF-GWAS). UF-GWAS utilized a Docker container to neatly package a JupyterLab instance which allowed users to run GWAS analyses quickly and easily (Aim 2). Next, we evaluated the usability of UF-GWAS by recruiting NGRs who performed task-based evaluations. We also tested the efficiency, accuracy, and cost of UF-GWAS against industry standard software. NGRs reported UF-GWAS as highly-usable and appreciated the following key components: clarity of the documentation, quick access to relevant background knowledge, ease of onboarding, and the shareability and reproducibility of results (Aim 3). Finally, we combined the many knowledge sources throughout this study to create a set of guidelines that future researchers can follow in order to create more usable informatics software. As NGRs and other researchers begin to enter the informatics landscape, it will become increasingly important to as informaticians to create more usable analysis software. By doing so, we can encourage robust experiments from a more diverse workforce, hopefully leading to an improvement in quality of care.