Stephen Piccolo, Ph.D.

Postdoctoral Fellow, Johnson Lab

Education

A.A.S., Natural Sciences, Ricks College, Rexburg, Idaho, 1998
B.S., Management Information Systems, Brigham Young University, Provo, Utah, 2001
Ph.D., Biomedical Informatics, University of Utah, Salt Lake City, Utah, 2011

Contact Information

Email: stephen.piccolo@hsc.utah.edu

Research Interests

Single-sample normalization methods for transcriptomic data. Single Channel Array Normalization (SCAN) is a microarray normalization method to facilitate personalized-medicine workflows. Rather than process microarray samples as groups, which can introduce biases and present logistical challenges (for example, if groups of samples had to be renormalized repeatedly in personalized-medicine workflows), SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-color (Affymetrix) microarrays from earlier generations (e.g. HG-U95 and HG-U133) and later generations (e.g., Gene ST and Exon ST).

The Universal Probability Code (UPC) method is an extension of SCAN that produces “barcode” values that estimate the probability a given gene is active in a specific sample. This method can be applied not only to one-color microarrays but also to two-color microarrays and RNA-sequencing data. As with SCAN, UPC is applied to individual samples.

For more information please visit: http://jlab.bu.edu/software/scan-upc/

Developing methods to predict breast cancer susceptibility in high-risk families. We are exploring the potential to predict breast cancer risk for individuals in high-risk families. These families may include individuals with an identified mutation in a known susceptibility gene. They may also include individuals for whom no drivers of risk are known. We are looking at ways to refine risk predictions for all such individuals.

Publications

Piccolo SR, Sun Y, Campbell JD, Lenburg ME, Bild AH, Johnson WE. A single-sample microarray normalization method to support personalized- medicine workflows. (in review)

Piccolo SR, Frey LJ. Clinical and molecular models of glioblastoma multiforme survival. International Journal of Data Mining and Bioinformatics 2012, in press.

Piccolo SR, Frey LJ. ML-Flex: A flexible framework for performing classification analyses in parallel. Journal of Machine Learning Research 2012, 13:555-559.

Frey LJ, Piccolo SR, Edgerton ME. Multiplicity: an organizing principle for cancers and somatic mutations. BMC Medical Genomics 2011, 4:52. doi:10.1186/1755-8794-4-52

Crockett DK, Piccolo SR, Ridge PG, Margraf RL, Lyon E, Williams MS, Mitchell JA. Predicting phenotypic severity of uncertain gene vari- ants in the RET proto-oncogene. PLoS ONE 2011, 6(3): e18380. doi:10.1371/journal.pone.0018380

Crockett DK, Piccolo SR, Narus SP, Mitchell JA, Facelli, JC. Computational feature selection and classification of RET phenotypic severity. J Data Mining in Genom Proteomics 2010, 1:103. doi:10.4172/2153-0602.1000103