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PMD: A Resource for Archiving and Analyzing Protein Microarray data.

ABSTRACT: Protein microarray is a powerful technology for both basic research and clinical study. However, because there is no database specifically tailored for protein microarray, the majority of the valuable original protein microarray data is still not publically accessible. To address this issue, we constructed Protein Microarray Database (PMD), which is specifically designed for archiving and analyzing protein microarray data. In PMD, users can easily browse and search the entire database by experimental name, protein microarray type, and sample information. Additionally, PMD integrates several data analysis tools and provides an automated data analysis pipeline for users. With just one click, users can obtain a comprehensive analysis report for their protein microarray data. The report includes preliminary data analysis, such as data normalization, candidate identification, and an in-depth bioinformatics analysis of the candidates, which include functional annotation, pathway analysis, and protein-protein interaction network analysis. PMD is now freely available at www.proteinmicroarray.cn.

SUBMITTER: Xu Z

PROVIDER: S-EPMC4728683 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Project description:Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.Gene expression signal intensities were similar after applying the log(2) or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33-48% of the variance), the RNA amplification batch (12-24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2-3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1-2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.

Dataset Information

PMD: A Resource for Archiving and Analyzing Protein Microarray data.

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