The thorny complexities of visualization research for clinical settings: A case study from genomics

Abstract

In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomics. We here describe four such challenge areas, elicited from a domain characterization effort in clinical genomics. We also list opportunities for visualization research to address clinical challenges in genomics that were uncovered in the case study. The findings are shown to have parallels with experiences from the diagnostic imaging domain.

Keywords: bioinformatics; cancer; copy number variant (CNV); genomics; next-generation sequencing; rare diseases; visual analytics (VA); visualization.

FIGURE 1

Overview of Next-generation sequencing (NGS) and Copy number variants (CNVs). (A) The NGS principles (B) Variant review pipeline, (C) CNV detection in NGS data. CNVs cause an increase in the number of fragments mapped to their area, which can skew the fraction of segments with different alleles.

FIGURE 2

The six themes characterizing variant interpretation created from interview data and the four elicited challenge areas for visualization research in clinical settings (bold), including their relation to the themes. Themes, from left to right: Variant confirmation (mainly ruling out false positives). Effects of limited time and resources. Constraints imposed by laws and regulations (ranging from IT security to validation of laboratory processes). Evaluating the clinical relevance of a variant. The interplay between the professions involved in the diagnostic work. Encodings and tools used for displaying CNVs.