qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.     

Relative quantification of mRNA: comparison of methods currently used for real-time PCR data analysis

Background  

Fluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.     

JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. This software tool has been developed in the open-source ITK software framework and is freely available under a GPL license. The article presents the complete image processing chain from raw data acquisition to 3D statistical group analysis. Results of the group comparison in the context of a study on spatial learning are shown as an illustration of the data that can be obtained with this tool.     

Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns

Single-cell quantitative real-time PCR (qRT-PCR) combined with high-throughput arrays allows the analysis of gene expression profiles at a molecular level in approximately 11 h after cell sample collection. We present here a high-content microfluidic real-time platform as a powerful tool for comparatively investigating the regulation of developmental processes in single cells. This approach overcomes the limitations involving heterogeneous cell populations and sample amounts, and may shed light on differential regulation of gene expression in normal versus disease-related contexts. Furthermore, high-throughput single-cell qRT-PCR provides a standardized, comparative assay for in-depth analysis of the mechanisms underlying human pluripotent stem cell self-renewal and differentiation.     

Application of public-domain statistical analysis software for evaluation and comparison of comet assay data

A novel approach for statistical analysis of comet assay data (i.e.: tail moment) is proposed, employing public-domain statistical software, the R system. The analytical strategy takes into account that the distribution of comet assay data, like the tail moment, is usually skewed and do not follow a normal distribution. Probability distributions used to model comet assay data included: the Weibull, the exponential, the logistic, the normal, the log normal and log-logistic distribution. In this approach it was also considered that heterogeneity observed among experimental units is a random feature of the comet assay data. This statistical model can be characterized with a location parameter m(ij), a scale parameter r and a between experimental units variability parameter theta. In the logarithmic scale, the parameter m(ij) depends additively on treatment and random effects, as follows: log(m(ij)) = a0 + a1x(ij) + b(i), where exp(a0) represents approximately the mean value of the control group, exp(a1) can be interpreted as the relative risk of damage with respect to the control group, x(ij) is an indicator of experimental group and exp(b(i)) is the individual risk effects assume to follows a Gamma distribution with mean 1 and variance theta. Model selection is based on Akaike's information criteria (AIC). Real data coming from comet analysis of blood samples taken from the flounder Paralichtys orbignyanus (Teleostei: Paralichtyidae) and from samples of cells suspension obtained from the estuarine polychaeta Laeonereis acuta (Nereididae) were employed. This statistical approach showed that the comet assay data should be analyzed under a modeling framework that take into account the important features of these measurements. Model selection and heterogeneity between experimental units play central points in the analysis of these data.     

EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data

The EpiGRAPH web service enables biologists to uncover hidden associations in vertebrate genome and epigenome datasets. Users can upload sets of genomic regions and EpiGRAPH will test multiple attributes (including DNA sequence, chromatin structure, epigenetic modifications and evolutionary conservation) for enrichment or depletion among these regions. Furthermore, EpiGRAPH learns to predictively identify similar genomic regions. This paper demonstrates EpiGRAPH's practical utility in a case study on monoallelic gene expression and describes its novel approach to reproducible bioinformatic analysis.     

Verification of reference genes for relative quantification of gene expression by real-time reverse transcription PCR in the pig

The aim of this study was to develop a set of reliable reference genes for quantification of mRNA expression in the pig. The mRNA expression stability in pig tissues was studied for 4 genes:EEF1A1, GAPDH, HPRT1 andTOP2B. The level of expression was characterized byCt values for each gene and each tissue. By using the geNorm algorithm, the stability of the reference genes was determined in the diaphragm, heart, kidney, liver, lungs, longissimus muscle, and spleen. On the basis of this information, suitable reference genes can be selected for mRNA expression studies in relevant pig tissues.     

The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/     

StAR: a simple tool for the statistical comparison of ROC curves

Background  

As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.     

ScreenClust: Advanced statistical software for supervised and unsupervised high resolution melting (HRM) analysis

Background: High resolution melting (HRM) is an emerging new method for interrogating and characterizing DNA samples. An important aspect of this technology is data analysis. Traditional HRM curves can be difficult to interpret and the method has been criticized for lack of statistical interrogation and arbitrary interpretation of results. Methods: Here we report the basic principles and first applications of a new statistical approach to HRM analysis addressing these concerns. Our method allows automated genotyping of unknown samples coupled with formal statistical information on the likelihood, if an unknown sample is of a known genotype (by discriminant analysis or “supervised learning”). It can also determine the assortment of alleles present (by cluster analysis or “unsupervised learning”) without a priori knowledge of the genotypes present. Conclusion: The new algorithms provide highly sensitive and specific auto-calling of genotypes from HRM data in both supervised an unsupervised analysis mode. The method is based on pure statistical interrogation of the data set with a high degree of standardization. The hypothesis-free unsupervised mode offers various possibilities for de novo HRM applications such as mutation discovery.