  Discovery of novel protein biomarkers for clinical applications is an active
research field across a manifold of diseases. Despite some successes and
progress, the biomarker development pipeline still frequently ends in failure
as biomarker candidates cannot be validated or translated to immunoassays.
Selection of strong disease biomarker candidates that further constitute
suitable targets for antibody binding in immunoassays is thus important. This
essential selection step can be supported and rationalized using bioinformatics
tools such as protein databases. Here, I present a workflow in the form of
decision trees to computationally investigate biomarker candidates and their
available affinity reagents in depth. This analysis can identify the most
promising biomarker candidates for assay development while minimal time and
effort is required.
