XcisClique: analysis of regulatory bicliques

BACKGROUND: Modeling of cis-elements or regulatory motifs in promoter (upstream) regions of genes is a challenging computational problem. In this work, set of regulatory motifs simultaneously present in the promoters of a set of genes is modeled as a biclique in a suitably defined bipartite graph. A biologically meaningful co-occurrence of multiple cis-elements in a gene promoter is assessed by the combined analysis of genomic and gene expression data. Greater statistical significance is associated with a set of genes that shares a common set of regulatory motifs, while simultaneously exhibiting highly correlated gene expression under given experimental conditions. METHODS: XcisClique, the system developed in this work, is a comprehensive infrastructure that associates annotated genome and gene expression data, models known cis-elements as regular expressions, identifies maximal bicliques in a bipartite gene-motif graph; and ranks bicliques based on their computed statistical significance. Significance is a function of the probability of occurrence of those motifs in a biclique (a hypergeometric distribution), and on the new sum of absolute values statistic (SAV) that uses Spearman correlations of gene expression vectors. SAV is a statistic well-suited for this purpose as described in the discussion. RESULTS: XcisClique identifies new motif and gene combinations that might indicate as yet unidentified involvement of sets of genes in biological functions and processes. It currently supports Arabidopsis thaliana and can be adapted to other organisms, assuming the existence of annotated genomic sequences, suitable gene expression data, and identified regulatory motifs. A subset of Xcis Clique functionalities, including the motif visualization component MotifSee, source code, and supplementary material are available at

A General Probabilistic Model of the PCR Process

This paper rigorously derives a general probabilistic model for the PCR process; this model includes as a special case the Velikanov-Kapral model where all nucleotide reaction rates are the same. In this model the probability of binding of deoxy-nucleoside triphosphate (dNTP) molecules with template strands is derived from the microscopic chemical kinetics. A recursive solution for the probability distribution of binding of dNTPs is developed for a single cycle and is used to calculate expected yield for a multicycle PCR. The model is able to reproduce important features of the PCR amplification process quantitatively. With a set of favorable reaction conditions, the amplification of the target sequence is fast enough to rapidly outnumber all side products. Furthemore, the final yield of the target sequence in a multicycle PCR run always approaches an asymptotic limit that is less than one. The amplification process itself is highly sensitive to initial concentrations and the reaction rates of addition to the template strand of each type of dNTP in the solution

Validation and Estimation of Parameters for a General Probabilistic Model of the PCR Process

Earlier work by Saha et al. rigorously derived a general probabilistic model for the PCR process that includes as a special case the Velikanov-Kapral model where all nucleotide reaction rates are the same. In this model the probability of binding of deoxy-nucleoside triphosphate (dNTP) molecules with template strands is derived from the microscopic chemical kinetics. A recursive solution for the probability function of binding of dNTPs is developed for a single cycle and is used to calculate expected yield for a multicycle PCR. The model is able to reproduce important features of the PCR amplification process quantitatively. With a set of favorable reaction conditions, the amplification of the target sequence is fast enough to rapidly outnumber all side products. Furthermore, the final yield of the target sequence in a multicycle PCR run always approaches an asymptotic limit that is less than one. The amplification process itself is highly sensitive to initial concentrations and the reaction rates of addition to the template strand of each type of dNTP in the solution. This paper extends the earlier Saha model with a physics based model of the dependence of the reaction rates on temperature, and estimates parameters in this new model by nonlinear regression. The calibrated model is validated using RT-PCR data. Key words: Levenberg-Marquardt algorithm, multicycle PCR, nonlinear regression, polymerase chain reaction (PCR), probabilistic model, yield. 1

Diagnostic criteria in Pai syndrome: results of a case series and a literature review

IF 2.164 (2017)International audiencePai syndrome was originally described as the association of a midline cleft lip, midline facial polyps, and lipoma of the central nervous system. However, only a few patients present with the full triad, and most exhibit a wide spectrum of phenotypic variability. The aim of this study was to phenotypically delineate Pai syndrome and to propose new criteria to facilitate a clinical diagnosis in the future. The study cohort consisted of seven case patients and an additional 60 cases diagnosed with Pai syndrome identified in a literature review. Only 23 of 67 patients presented the full triad as historically described by Pai et al. (1987). A congenital facial midline skin mass was always encountered, particularly affecting the nasal structures (60/67). A midline facial cleft was reported in 45 of 67 patients and a pericallosal lipoma in 42 of 67 patients. The proposed definition of Pai syndrome is the association of (1) a congenital nasal and/or mediofrontal skin mass and/or a mid-anterior alveolar process polyp as a mandatory criterion, and at least one of the following criteria: (2) midline cleft lip and/or midline alveolar cleft, and/or (3) a pericallosal lipoma or interhemispheric lipoma in the case of corpus callosum dysgenesis