Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation and useful visualization tools are demonstrated via case study of complex biological samples assessed using the iTRAQ™ relative labeling protocol. KeywordsProteomics; ANOVA; iTRAQ™; Normalization; relative labeling protocol; Missing data; GaussSiedel; Backfitting; Fixed effects model; Mixed effects model A. INTRODUCTIONThe objective of global proteomics via mass spectrometry is to detect and quantify all proteins present in a biological sample. Proteins that exhibit an increase/decrease in abundance between two or more groups of interest, (e.g., diseased and non-diseased) are considered candidate CORRESPONDING AUTHOR FOOTNOTE Ann L. Oberg, Mayo Clinic, Cancer Center Statistics, 200 First St SW, Rochester, MN 55905. Telephone (507) 538-1556; Fax (507) biomarkers. However, experimental factors such as differences in sample collection, sample characteristics such as cellular concentration, variations in sample processing and the experimental process add variability to the observed abundances. Experimental variability hinders the comparison of effects of interest, and if not accounted for during the design and analysis stages, can lead the researcher down an erroneous path of discovery.Several mass spectrometry (MS) techniques have been developed that allow greater control over experimental factors that introduce variability and ultimately decrease the quality of the data. Recently, focus has centered on the ability to assess multiple samples within a single MS experiment. Binary sample labeling techniques such as 16 O/ 18 O (1) , ICAT™ (2) , and SILAC (3) were developed to evaluate paired samples whereas iTRAQ™ (4) was developed to simultaneously analyze four, and more recently, eight samples (5) . The binary labeling techniques add complexity to the acquired spectra and to their interpretation by introducing additional peaks into the mass spectra. Furthermore, overlapping isotopic clusters require further analytical techniques to deconvolute the resulting spectrum and the associated protein/ peptide abundances (4,6,7) . The iTRAQ™ labeling system overcomes this to some extent since the labeled species are isobaric and protein abundances are measured only in the resulting MS/ MS fragmentation spectra.Although sample labeling techniques allow greater control over experimental variability within an MS experiment, the analysis of multiple MS experiments remains difficult. Within an experiment, it is important that equal amounts of total protein are labeled under each labeling condition to ensure that the observed abundances are not influenced by total protein concentration. Once the samples are labeled and mixed together for MS analysis, labeling methods naturally control for instrument variability. The same principles apply when performing multiple experiments; with th...

The mechanism by which trinucleotide expansion occurs in human genes is not understood. However, it has been hypothesized that DNA secondary structure may actively participate by preventing FEN-1 cleavage of displaced Okazaki fragments. We show here that secondary structure can, indeed, play a role in expansion by a FEN-1-dependent mechanism. Secondary structure inhibits flap processing at CAG, CGG, or CTG repeats in a length-dependent manner by concealing the 5' end of the flap that is necessary for both binding and cleavage by FEN-1. Thus, secondary structure can defeat the protective function of FEN-1, leading to site-specific expansions. However, when FEN-1 is absent from the cell, alternative pathways to simple inhibition of flap processing contribute to expansion.