Prediction of a Missing Protein Expression Map in the Context of the Human Proteome Project

Experimental evidence for the entire
human proteome has been defined
in the Human Proteome Project, and it is publicly available in the
neXtProt database. However, there are still human proteins for which
reliable experimental evidence does not exist, and the identification
of such information has become one of the overriding objectives in
the chromosome-centric study of the human proteome. With this aim
and considering the complexity of protein detection using shotgun
and targeted proteomics, the research community has addressed the
integration of transcriptomics and proteomics landscapes. Here, we
describe an analytical pipeline that predicts the probability of a
missing protein being expressed in a biological sample based on (1)
gene sequence characteristics, (2) the probability of an expressed
gene being a coding gene of a missing protein in a certain sample,
and (3) the probability of a gene being expressed in a transcriptomic
experiment. More than 3400 microarray experiments were analyzed corresponding
to three biological sources: cell lines, normal tissues, and cancer
samples. A gene classification based on gene expression profiles distinguished
among ubiquitous, nonubiquitous, nonexpressed, and coding genes of
missing proteins. In addition, a different tissue-specific expression
pattern for the coding genes of missing proteins is reported. Our
results underline the relevance of selecting an appropriate sample
for the detection of missing proteins and provide a comprehensive
method to score their expression probability. Testis, brain, and skeletal
muscle are the most promising normal tissues.