117,683 research outputs found

    An island-based approach for RNA-SEQ differential expression analysis.

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    High-throughput mRNA sequencing (also known as RNA-Seq) promises to be the technique of choice for studying transcriptome profiles, offering several advantages over old techniques such as microarrays. This technique provides the ability to develop precise methodologies for a variety of RNA-Seq applications including gene expression quantification, novel transcript and exon discovery, differential expression (DE) and splice variant detection. The detection of significantly changing features (e.g. genes, transcript isoforms, exons) in expression across biological samples is a primary application of RNA-Seq. Uncovering which features are significantly differentially expressed between samples can provide insight into their functions. One major limitation with the majority of recently developed methods for RNA-Seq differential expression is the dependency on annotated biological features to detect expression differences across samples. This forces the identification of expression levels and the detection of significant changes to known genomic regions. Thus, any significant changes occurring in unannotated regions will not be captured. To overcome this limitation, we developed a novel segmentation approach, Island-Based (IBSeq), for analyzing differential expression in RNA-Seq and targeted sequencing (exome capture) data without specific knowledge of an isoform. IBSeq segmentation determines individual islands of expression based on windowed read counts that can be compared across experimental conditions to determine differential island expression. In order to detect differentially expressed features, the significance of DE islands corresponding to each feature are combined using combined p-value methods. We evaluated the performance of our approach by comparing it to a number of existing gene DE methods using several benchmark MAQC RNA-Seq datasets. Using the area under ROC curve (auROC) as a performance metric, results show that IBSeq clearly outperforms all other methods compared

    DNMT inhibitors reverse a specific signature of aberrant promoter DNA methylation and associated gene silencing in AML

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    <b>Background</b>. Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are neoplastic disorders of hematopoietic stem cells. DNA methyltransferase inhibitors (DNMTi), 5-azacytidine (AzaC) and 5-aza-2’-deoxycytidine (Decitabine), benefit some MDS/AML patients. However, the role of DNMTi-induced DNA hypomethylation in regulation of gene expression in AML is unclear.<p></p> <b>Results. </b> We compared the effects of AzaC on DNA methylation and gene expression using whole-genome single-nucleotide bisulfite-sequencing (WGBS) and RNA-sequencing in OCI-AML3 (AML3) cells. For data analysis, we used an approach recently developed for discovery of differential patterns of DNA methylation associated with changes in gene expression, that is tailored to single-nucleotide bisulfite-sequencing data (Washington University Interpolated Methylation Signatures (WIMSi)). By this approach, a subset of genes upregulated by AzaC was found to be characterized by AzaC-induced signature methylation loss flanking the transcription start site. These genes are enriched for genes increased in methylation and decreased in expression in AML3 cells compared to normal hematopoietic stem and progenitor cells. Moreover, these genes are preferentially upregulated by Decitabine in human primary AML blasts, and control cell proliferation, death and development. <p></p> <b>Conclusions.</b> Our WGBS and WIMSi data analysis approach has identified a set of genes whose is methylation and silencing in AML is reversed by DNMTi. These genes are good candidates for direct regulation by DNMTi, and their reactivation by DNMTi may contribute to therapeutic activity. This study also demonstrates the ability of WIMSi to reveal relationships between DNA methylation and gene expression, based on single-nucleotide bisulfite-sequencing and RNA-seq data.<p></p&gt

    Fixation of genetic variation and optimization of gene expression: The speed of evolution in isolated lizard populations undergoing Reverse Island Syndrome

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    The ecological theory of island biogeography suggests that mainland populations should be more genetically divergent from those on large and distant islands rather than from those on small and close islets. Some island populations do not evolve in a linear way, but the process of divergence occurs more rapidly because they undergo a series of phenotypic changes, jointly known as the Island Syndrome. A special case is Reversed Island Syndrome (RIS), in which populations show drastic phenotypic changes both in body shape, skin colouration, age of sexual maturity, aggressiveness, and food intake rates. The populations showing the RIS were observed on islets nearby mainland and recently raised, and for this they are useful models to study the occurrence of rapid evolutionary change. We investigated the timing and mode of evolution of lizard populations adapted through selection on small islets. For our analyses, we used an ad hoc model system of three populations: wild-type lizards from the mainland and insular lizards from a big island (Capri, Italy), both Podarcis siculus siculus not affected by the syndrome, and a lizard population from islet (Scopolo) undergoing the RIS (called P. s. coerulea because of their melanism). The split time of the big (Capri) and small (Scopolo) islands was determined using geological events, like sea-level rises. To infer molecular evolution, we compared five complete mitochondrial genomes for each population to reconstruct the phylogeography and estimate the divergence time between island and mainland lizards. We found a lower mitochondrial mutation rate in Scopolo lizards despite the phenotypic changes achieved in approximately 8,000 years. Furthermore, transcriptome analyses showed significant differential gene expression between islet and mainland lizard populations, suggesting the key role of plasticity in these unpredictable environments

    Pulsation modes in rapidly rotating stellar models based on the Self-Consistent Field method

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    Context: New observational means such as the space missions CoRoT and Kepler and ground-based networks are and will be collecting stellar pulsation data with unprecedented accuracy. A significant fraction of the stars in which pulsations are observed are rotating rapidly. Aims: Our aim is to characterise pulsation modes in rapidly rotating stellar models so as to be able to interpret asteroseismic data from such stars. Methods: The pulsation code developed in Ligni\`eres et al. (2006) and Reese et al. (2006) is applied to stellar models based on the self-consistent field (SCF) method (Jackson et al. 2004, 2005, MacGregor et al. 2007). Results: Pulsation modes in SCF models follow a similar behaviour to those in uniformly rotating polytropic models, provided that the rotation profile is not too differential. Pulsation modes fall into different categories, the three main ones being island, chaotic, and whispering gallery modes, which are rotating counterparts to modes with low, medium, and high l-|m| values, respectively. The frequencies of the island modes follow an asymptotic pattern quite similar to what was found for polytropic models. Extending this asymptotic formula to higher azimuthal orders reveals more subtle behaviour as a function of m and provides a first estimate of the average advection of pulsation modes by rotation. Further calculations based on a variational principle confirm this estimate and provide rotation kernels that could be used in inversion methods. When the rotation profile becomes highly differential, it becomes more and more difficult to find island and whispering gallery modes at low azimuthal orders. At high azimuthal orders, whispering gallery modes, and in some cases island modes, reappear.Comment: 16 pages, 11 figures, accepted for publication in A&

    Pancancer analysis of DNA methylation-driven genes using MethylMix.

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    Aberrant DNA methylation is an important mechanism that contributes to oncogenesis. Yet, few algorithms exist that exploit this vast dataset to identify hypo- and hypermethylated genes in cancer. We developed a novel computational algorithm called MethylMix to identify differentially methylated genes that are also predictive of transcription. We apply MethylMix to 12 individual cancer sites, and additionally combine all cancer sites in a pancancer analysis. We discover pancancer hypo- and hypermethylated genes and identify novel methylation-driven subgroups with clinical implications. MethylMix analysis on combined cancer sites reveals 10 pancancer clusters reflecting new similarities across malignantly transformed tissues

    The Cyprinodon variegatus genome reveals gene expression changes underlying differences in skull morphology among closely related species

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    Genes in durophage intersection set at 15 dpf. This is a comma separated table of the genes in the 15 dpf durophage intersection set. Given are edgeR results for each pairwise comparison. Columns indicating whether a gene is included in the intersection set at a threshold of 1.5 or 2 fold are provided. (CSV 13 kb

    Capture numbers and islands size distributions in models of submonolayer surface growth

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    The capture numbers entering the rate equations (RE) for submonolayer film growth are determined from extensive kinetic Monte Carlo (KMC) simulations for simple representative growth models yielding point, compact, and fractal island morphologies. The full dependence of the capture numbers on island size, and on both the coverage and the D/F ratio between the adatom diffusion coefficient D and deposition rate F is determined. Based on this information, the RE are solved to give the RE island size distribution (RE-ISD). The RE-ISDs are shown to agree well with the corresponding KMC-ISDs for all island morphologies. For compact morphologies, however, this agreement is only present for coverages smaller than about 5% due to a significantly increased coalescence rate compared to fractal morphologies. As found earlier, the scaled KMC-ISDs as a function of scaled island size approach, for fixed coverage, a limiting curve for D/F going to infinity. Our findings provide evidence that the limiting curve is independent of the coverage for point islands, while the results for compact and fractal island morphologies indicate a dependence on the coverage.Comment: 13 pages, 12 figure

    Fisher Waves: an individual based stochastic model

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    The propagation of a beneficial mutation in a spatially extended population is usually studied using the phenomenological stochastic Fisher-Kolmogorov (SFKPP) equation. We derive here an individual based, stochastic model founded on the spatial Moran process where fluctuations are treated exactly. At high selection pressure, the results of this model are different from the classical FKPP. At small selection pressure, the front behavior can be mapped into a Brownian motion with drift, the properties of which can be derived from microscopic parameters of the Moran model. Finally, we show that the diffusion coefficient and the noise amplitude of SFKPP are not independent parameters but are both determined by the dispersal kernel of individuals
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