How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results

BACKGROUND: Short oligonucleotide arrays for transcript profiling have been available for several years. Generally, raw data from these arrays are analysed with the aid of the Microarray Analysis Suite or GeneChip Operating Software (MAS or GCOS) from Affymetrix. Recently, more methods to analyse the raw data have become available. Ideally all these methods should come up with more or less the same results. We set out to evaluate the different methods and include work on our own data set, in order to test which method gives the most reliable results. RESULTS: Calculating gene expression with 6 different algorithms (MAS5, dChip PMMM, dChip PM, RMA, GC-RMA and PDNN) using the same (Arabidopsis) data, results in different calculated gene expression levels. Consequently, depending on the method used, different genes will be identified as differentially regulated. Surprisingly, there was only 27 to 36% overlap between the different methods. Furthermore, 47.5% of the genes/probe sets showed good correlation between the mismatch and perfect match intensities. CONCLUSION: After comparing six algorithms, RMA gave the most reproducible results and showed the highest correlation coefficients with Real Time RT-PCR data on genes identified as differentially expressed by all methods. However, we were not able to verify, by Real Time RT-PCR, the microarray results for most genes that were solely calculated by RMA. Furthermore, we conclude that subtraction of the mismatch intensity from the perfect match intensity results most likely in a significant underestimation for at least 47.5% of the expression values. Not one algorithm produced significant expression values for genes present in quantities below 1 pmol. If the only purpose of the microarray experiment is to find new candidate genes, and too many genes are found, then mutual exclusion of the genes predicted by contrasting methods can be used to narrow down the list of new candidate genes by 64 to 73%

Functional health status in subjects after a motor vehicle accident, with emphasis on whiplash associated disorders: design of a descriptive, prospective inception cohort study

Contains fulltext : 70254.pdf (publisher's version ) (Open Access)BACKGROUND: The clinical consequences of whiplash injuries resulting from a motor vehicle accident (MVA) are poorly understood. Thereby, there is general lack of research on the development of disability in patients with acute and chronic Whiplash Associated Disorders. METHODS/DESIGN: The objective is to describe the design of an inception cohort study with a 1-year follow-up to determine risk factors for the development of symptoms after a low-impact motor vehicle accident, the prognosis of chronic disability, and costs. Victims of a low-impact motor vehicle accident will be eligible for participation. Participants with a Neck Disability Index (NDI) score of 7 or more will be classified as experiencing post-traumatic neck pain and will enter the experimental group. Participants without complaints (a NDI score less than 7) will enter the reference group. The cohort will be followed up by means of postal questionnaires and physical examinations at baseline, 3 months, 6 months, and 12 months. Recovery from whiplash-associated disorders will be measured in terms of perceived functional health, and employment status (return to work). Life tables will be generated to determine the 1-year prognosis of whiplash-associated disorders, and risk factors and prognostic factors will be assessed using multiple logistic regression analysis. DISCUSSION: Little is known about the development of symptoms and chronic disability after a whiplash injury. In the clinical setting, it is important to identify those people who are at risk of developing chronic symptoms.This inception prospective cohort study will provide insight in the influence of risk factors, of the development of functional health problems, and costs in people with whiplash-associated disorders

A transcriptome-wide association study among 97,898 women to identify candidate susceptibility genes for epithelial ovarian cancer risk

Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in non-coding regions, and causal genes underlying these associations remain largely unknown. Here we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian-tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P<2.2×10-6, we identified 35 genes including FZD4 at 11q14.2 (Z=5.08, P=3.83×10-7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly-associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and 3 genes remained (P<1.47 x 10-3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-4

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p> GC-RMA and PDNN. Reproducibility is calculated as the standard deviation divided by the average signal, which is the coefficient of variation (CV). The CV values are sorted from low to high. The PM, RMA and PDNN algorithms are giving the best reproducible results and MAS 5.0 the worst. Reproducibility of the two other replicated treatments ethylene and low-light gave similar results (data not shown)

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-0

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>ethod used to calculate gene expression. This diagram shows exactly the differences and similarities between all the methods. PDNN, MAS 5.0 (MAS, or GCOS), dChip PMMM (PMMM), dChip PM only (PM), RMA and GC-RMA were used. Only 790 genes were in common for all four algorithms. Comparable results were obtained from the low-light treatment. Areas with one letter shows genes which are unique for one method, areas with two letters shows genes which are only in common between these two methods, and so on

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-5

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>ignal intensity is smaller than the PM signal. In panel A and B there is no correlation between the PM and MM signals as can been seen by the low slope and Pearson correlation coefficient. This in contrast to results in panel C and D were the slope and Pearson correlation coefficient are large. These signals are obtained from the microarray scanner and are the input for the six calculation methods

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results-3

<p><b>Copyright information:</b></p><p>Taken from "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results"</p><p>BMC Bioinformatics 2006;7():137-137.</p><p>Published online 15 Mar 2006</p><p>PMCID:PMC1431565.</p><p>Copyright © 2006 Millenaar et al; licensee BioMed Central Ltd.</p>eriment. The observed concentrations are adjusted so that all lines have the same intercept at a ln concentration of 2.8 (16 pmol). The solid line without symbols represents the ideal slope-1 line. () The accuracy of picking up the spiked-in genes. The significance between two successive spike-in concentrations (0–0.125; 0.125–0.25; etc.) was calculated for each gene. The number of genes where calculated per spike-in concentration that significantly where up regulated, and presented on the y-axis as percentage. This means that at "1" all 42 genes where significant at a given concentration