Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data.

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as 'fold-difference' results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Coronary MR angiography: comparison of quantitative and qualitative data from four techniques.

OBJECTIVE: The optimal coronary MR angiography sequence has yet to be determined. We sought to quantitatively and qualitatively compare four coronary MR angiography sequences. SUBJECTS AND METHODS. Free-breathing coronary MR angiography was performed in 12 patients using four imaging sequences (turbo field-echo, fast spin-echo, balanced fast field-echo, and spiral turbo field-echo). Quantitative comparisons, including signal-to-noise ratio, contrast-to-noise ratio, vessel diameter, and vessel sharpness, were performed using a semiautomated analysis tool. Accuracy for detection of hemodynamically significant disease (> 50%) was assessed in comparison with radiographic coronary angiography. RESULTS: Signal-to-noise and contrast-to-noise ratios were markedly increased using the spiral (25.7 +/- 5.7 and 15.2 +/- 3.9) and balanced fast field-echo (23.5 +/- 11.7 and 14.4 +/- 8.1) sequences compared with the turbo field-echo (12.5 +/- 2.7 and 8.3 +/- 2.6) sequence (p < 0.05). Vessel diameter was smaller with the spiral sequence (2.6 +/- 0.5 mm) than with the other techniques (turbo field-echo, 3.0 +/- 0.5 mm, p = 0.6; balanced fast field-echo, 3.1 +/- 0.5 mm, p < 0.01; fast spin-echo, 3.1 +/- 0.5 mm, p < 0.01). Vessel sharpness was highest with the balanced fast field-echo sequence (61.6% +/- 8.5% compared with turbo field-echo, 44.0% +/- 6.6%; spiral, 44.7% +/- 6.5%; fast spin-echo, 18.4% +/- 6.7%; p < 0.001). The overall accuracies of the sequences were similar (range, 74% for turbo field-echo, 79% for spiral). Scanning time for the fast spin-echo sequences was longest (10.5 +/- 0.6 min), and for the spiral acquisitions was shortest (5.2 +/- 0.3 min). CONCLUSION: Advantages in signal-to-noise and contrast-to-noise ratios, vessel sharpness, and the qualitative results appear to favor spiral and balanced fast field-echo coronary MR angiography sequences, although subjective accuracy for the detection of coronary artery disease was similar to that of other sequences.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Selecting control genes for RT-QPCR using public microarray data.

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

Suppression of Arabidopsis protophloem differentiation and root meristem growth by CLE45 requires the receptor-like kinase BAM3.

Peptide signaling presumably occupies a central role in plant development, yet only few concrete examples of receptor-ligand pairs that act in the context of specific differentiation processes have been described. Here we report that second-site null mutations in the Arabidopsis leucine-rich repeat receptor-like kinase gene barely any meristem 3 (BAM3) perfectly suppress the postembryonic root meristem growth defect and the associated perturbed protophloem development of the brevis radix (brx) mutant. The roots of bam3 mutants specifically resist growth inhibition by the CLAVATA3/ENDOSPERM SURROUNDING REGION 45 (CLE45) peptide ligand. WT plants transformed with a construct for ectopic overexpression of CLE45 could not be recovered, with the exception of a single severely dwarfed and sterile plant that eventually died. By contrast, we obtained numerous transgenic bam3 mutants transformed with the same construct. These transgenic plants displayed a WT phenotype, however, supporting the notion that CLE45 is the likely BAM3 ligand. The results correlate with the observation that external CLE45 application represses protophloem differentiation in WT, but not in bam3 mutants. BAM3, BRX, and CLE45 are expressed in a similar spatiotemporal trend along the developing protophloem, up to the end of the transition zone. Induction of BAM3 expression upon CLE45 application, ectopic overexpression of BAM3 in brx root meristems, and laser ablation experiments suggest that intertwined regulatory activity of BRX, BAM3, and CLE45 could be involved in the proper transition of protophloem cells from proliferation to differentiation, thereby impinging on postembryonic growth capacity of the root meristem.

Association of urinary calcium excretion with serum calcium and vitamin D levels.

BACKGROUND AND OBJECTIVES: Population-based data on urinary calcium excretion are scarce. The association of serum calcium and circulating levels of vitamin D [25(OH)D2 or D3] with urinary calcium excretion in men and women from a population-based study was explored. DESIGN, SETTINGS, PARTICIPANTS, & MEASUREMENTS: Multivariable linear regression was used to explore factors associated with square root-transformed 24-hour urinary calcium excretion (milligrams per 24 hours) taken as the dependent variable with a focus on month-specific vitamin D tertiles and serum calcium in the Swiss Survey on Salt Study. RESULTS: In total, 624 men and 669 women were studied with mean ages of 49.2 and 47.0 years, respectively (age range=15-95 years). Mean urinary calcium excretion was higher in men than in women (183.05 versus 144.60 mg/24 h; P<0.001). In adjusted models, the association (95% confidence interval) of square root urinary calcium excretion with protein-corrected serum calcium was 1.78 (95% confidence interval, 1.21 to 2.34) mg/24 h per milligram per deciliter in women and 0.59 (95% confidence interval, -0.11 to 1.29) mg/24 h per milligram per deciliter in men. Men in the third 25(OH)D3 tertile had higher square root urinary calcium excretion than men in the first tertile (0.99; 95% confidence interval, 0.36 to 1.63 mg/24 h per nanogram per milliliter), and the corresponding association was 0.32 (95% confidence interval, -0.22 to 0.85) mg/24 h per nanogram per milliliter in women. These sex differences were more marked under conditions of high urinary sodium or urea excretions. CONCLUSIONS: There was a positive association of serum calcium with urinary calcium excretion in women but not men. Vitamin 25(OH)D3 was associated with urinary calcium excretion in men but not women. These results suggest important sex differences in the hormonal and dietary control of urinary calcium excretion.