iomarker panels are becoming increasingly important clinical tools. In the research lab they also have a wide array of
applications, including the identification and characterization of unknown and experimentally manipulated samples. In
order to keep cost down and facilitate high sample throughput, biomarker panels are often limited to a relatively small number of
markers. Typically these are handpicked genes deemed important or informative by the researcher. However, without statistical
support that the most informative biomarkers have been selected, biomarker panels can be subject to extensive sampling bias,
resulting in wasted resources and misclassification. Moreover, even in the presence of proper biomarker selection, the accurate
interpretation of marker profiles is often difficult and frequently undertaken without statistical rigor. Here we present a pipeline
for the rational, easy, and statistically-robust design and interpretation of biomarker panels. The first half of the pipeline consists
of an algorithm designed to identify the minimum unique biomarker profile (MUMP) necessary to accurately and efficiently
classify sample types within a set of given experimental constraints. The second part of the pipeline provides statistically robust
matching of biomarker profiles to reference samples, providing a statistically defensible interpretation of the biomarker assay.
Single-blind testing on samples of differentiating stem cells shows that the MUMPs pipeline can accurately identify cells in the lab.
The MUMPs pipeline is reliable, easy, and cost-effective tool that will allow bench scientists to readily implement computationally
supported rational design of biomarker assays.
James Lindsay is a Ph.D. student at the University of Connecticut in the Computer Science and Engineering department. James is also the CTO of
Smpl Bio, a bioinformatics company commercializing software to simplify the design and analysis of genetic tests
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