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Alternative splicing (AS) plays a key role in cancer: all its hallmarks have been associated with different mechanisms of abnormal AS. The improvement of the human transcriptome annotation and the availability of fast and accurate software to estimate isoform concentrations has boosted the analysis of transcriptome profiling from RNA-seq. The statistical analysis of AS is a challenging problem not yet fully solved. We have included in EventPointer (EP), a Bioconductor package, a novel statistical method that can use the bootstrap of the pseudoaligners. We compared it with other state-of-the-art algorithms to analyze AS. Its performance is outstanding for shallow sequencing conditions. The statistical framework is very flexible since it is based on design and contrast matrices. EP now includes a convenient tool to find the primers to validate the discoveries using PCR. We also added a statistical module to study alteration in protein domain related to AS. Applying it to 9514 patients from TCGA and TARGET in 19 different tumor types resulted in two conclusions: i) aberrant alternative splicing alters the relative presence of Protein domains and, ii) the number of enriched domains is strongly correlated with the age of the patients.

Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales., Alexander Gross et al. present ProbRules, a dynamic modeling approach that combines probabilities and logical rules to represent network dynamics over multiple scales. They apply ProbRules to a Wnt network, predicting gene expression readouts that they confirm with wet-lab experiments.

The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction to data modeling. However, some issues exist within the process that have rarely been talked about. Rice et al. discussed some issues in plasma miRNA studies, such as the lack of standard methodology including the use of different cycle threshold, time to plasma extraction, among others. These issues can lead to inconsistent data, and thus impact the result and assay reproducibility. Other external issues, such as batch effect and operator effect, may also indirectly impact the statistical analysis. Here, we discuss issues in plasma miRNA studies from a statistical point of view. The interaction effect of different ways of calculating fold-change, the choice of housekeeping genes, and methods of normalization are among the issues we discuss, with data demonstrations. P values are calculated and compared to determine the effect of those issues on statistical conclusions. Statistical methods such as analysis of variance and analysis of covariance are crucial in the analysis of miRNA but investigators are often confused about them; therefore, a brief explanation of these statistical methods is also included. In addition, 3-group classification is discussed, as it is often challenging, compared with 2-group classification.

Objective: Systematic tracking of microRNA (miRNA) targets remains a challenge. In our work, we aimed to use TargetScore to investigate the potential targets of miRNA203 and miRNA-146a in psoriasis by integrating miRNA overexpression information and sequence data, and to further uncover the functions of miRNA203 and miRNA-146a in psoriasis. Methods: This was a case-control bioinformatics analysis using already published microarray data of psoriasis. We calculated targetScores by combining log fold-change and sequence scores obtained from TargetScan context score, probabilities of conserved targeting, and derived the distribution of targetScores. The scoring cutoff was chosen based on the different targetScore distributions for the nonvalidated and validated targets. The potential target genes for miRNA-203 and miRNA-146a were predicted based on the targetScore threshold. To reveal the functions of miRNA-203 and miRNA-146a, we implemented pathway enrichment analyses for the targets of miRNA-203 and miRNA-146a. Results: TargetScore >0.4 was selected as the threshold to filter out less confidence targets because we observed little overlap between the 2 distribution at targetScore = 0.4. Based on the targetScore >0.4, 49 target genes for miRNA-203 and 17 targets for miRNA-146a were identified. Pathway enrichment results showed that the target genes of miRNA-203 (including KIR2DL1, HLA-DQA1, KIR3DL1) only participated in antigen processing and presentation. The target genes of miRNA-146a (covering ADORA3, CYSLTR2, HRH4) were only involved in neuroactive ligand-receptor interaction. Conclusion: MiRNA203 and miRNA-146a played important roles in psoriasis progression, partially through regulating the pathways of antigen processing and presentation, and neuroactive ligand-receptor interaction, respectively.

Background The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed “transcript pattern”. A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. Results In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80 % fold-change concordance with the gold standard qRT-PCR (TaqMan). Conclusions This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1913-6) contains supplementary material, which is available to authorized users.

Biological systems produce outputs in response to variable inputs. Input-output relations tend to follow a few regular patterns. For example, many chemical processes follow the S-shaped Hill equation relation between input concentrations and output concentrations. That Hill equation pattern contradicts the fundamental Michaelis-Menten theory of enzyme kinetics. I use the discrepancy between the expected Michaelis-Menten process of enzyme kinetics and the widely observed Hill equation pattern of biological systems to explore the general properties of biological input-output relations. I start with the various processes that could explain the discrepancy between basic chemistry and biological pattern. I then expand the analysis to consider broader aspects that shape biological input-output relations. Key aspects include the input-output processing by component subsystems and how those components combine to determine the system's overall input-output relations. That aggregate structure often imposes strong regularity on underlying disorder. Aggregation imposes order by dissipating information as it flows through the components of a system. The dissipation of information may be evaluated by the analysis of measurement and precision, explaining why certain common scaling patterns arise so frequently in input-output relations. I discuss how aggregation, measurement and scale provide a framework for understanding the relations between pattern and process. The regularity imposed by those broader structural aspects sets the contours of variation in biology. Thus, biological design will also tend to follow those contours. Natural selection may act primarily to modulate system properties within those broad constraints., Comment: Biology Direct 8:31

International audience; The problem of identifying differential activity such as in gene expression is a major defeat in biostatistics and bioinformatics. Equally important, however much less frequently studied, is the question of similar activity from one biological condition to another. The fold-change, or ratio, is usually considered a relevant criterion for stating difference and similarity between measurements. Importantly, no statistical method for concomitant evaluation of similarity and distinctness currently exists for biological applications. Modern microarray, digital PCR (dPCR), and Next-Generation Sequencing (NGS) technologies frequently provide a means of coefficient of variation estimation for individual measurements. Using fold-change, and by making the assumption that measurements are normally distributed with known variances, we designed a novel statistical test that allows us to detect concomitantly, thus using the same formalism, differentially and similarly expressed genes (http://cds.ihes.fr). Given two sets of gene measurements in different biological conditions, the probabilities of making type I and type II errors in stating that a gene is differentially or similarly expressed from one condition to the other can be calculated. Furthermore, a confidence interval for the fold-change can be delineated. Finally, we demonstrate that the assumption of normality can be relaxed to consider arbitrary distributions numerically. The Concomitant evaluation of Distinctness and Similarity (CDS) statistical test correctly estimates similarities and differences between measurements of gene expression. The implementation, being time and memory efficient, allows the use of the CDS test in high-throughput data analysis such as microarray, dPCR, and NGS experiments. Importantly, the CDS test can be applied to the comparison of single measurements (N = 1) provided the variance (or coefficient of variation) of the signals is known, making CDS a valuable tool also in biomedical analysis where typically a single measurement per subject is available.

La technologie des microarrays demeure à ce jour un outil important pour la mesure de l'expression génique. Au-delà de la technologie elle-même, l'analyse des données provenant des microarrays constitue un problème statistique complexe, ce qui explique la myriade de méthodes proposées pour le pré-traitement et en particulier, l'analyse de l'expression différentielle. Toutefois, l'absence de données de calibration ou de méthodologie de comparaison appropriée a empêché l'émergence d'un consensus quant aux méthodes d'analyse optimales. En conséquence, la décision de l'analyste de choisir telle méthode plutôt qu'une autre se fera la plupart du temps de façon subjective, en se basant par exemple sur la facilité d'utilisation, l'accès au logiciel ou la popularité. Ce mémoire présente une approche nouvelle au problème de la comparaison des méthodes d'analyse de l'expression différentielle. Plus de 800 pipelines d'analyse sont appliqués à plus d'une centaine d'expériences sur deux plateformes Affymetrix différentes. La performance de chacun des pipelines est évaluée en calculant le niveau moyen de co-régulation par l'entremise de scores d'enrichissements pour différentes collections de signatures moléculaires. L'approche comparative proposée repose donc sur un ensemble varié de données biologiques pertinentes, ne confond pas la reproductibilité avec l'exactitude et peut facilement être appliquée à de nouvelles méthodes. Parmi les méthodes testées, la supériorité de la sommarisation FARMS et de la statistique de l'expression différentielle TREAT est sans équivoque. De plus, les résultats obtenus quant à la statistique d'expression différentielle corroborent les conclusions d'autres études récentes à propos de l'importance de prendre en compte la grandeur du changement en plus de sa significativité statistique., Microarrays remain an important tool for the measurement of gene expression, and a myriad of methods for their pre-processing or statistical testing of differential expression has been proposed in the past. However, insufficient and sometimes contradictory evidence has prevented the emergence of a strong consensus over a preferred methodology. This leaves microarray practitioners to somewhat arbitrarily decide which method should be used to analyze their data. Here we present a novel approach to the problem of comparing methods for the identification of differentially expressed genes. Over eight hundred analytic pipelines were applied to more than a hundred independent microarray experiments. The accuracy of each analytic pipeline was assessed by measuring the average level of co-regulation uncovered across all data sets. This analysis thus relies on a varied set of biologically relevant data, does not confound reproducibility for accuracy and can easily be extended to future analytic pipelines. This procedure identified FARMS summarization and the TREAT gene ordering statistic as algorithms significantly more accurate than other alternatives. Most interestingly, our results corroborate recent findings about the importance of taking the magnitude of change into account along with an assessment of statistical significance.