1,114 research outputs found

    A Graph-Theoretical Approach to the Selection of the Minimum Tiling Path from a Physical Map

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    The problem of computing the minimum tiling path (MTP) from a set of clones arranged in a physical map is a cornerstone of hierarchical (clone-by-clone) genome sequencing projects. We formulate this problem in a graph theoretical framework, and then solve by a combination of minimum hitting set and minimum spanning tree algorithms. The tool implementing this strategy, called FMTP, shows improved performance compared to the widely used software FPC. When we execute FMTP and FPC on the same physical map, the MTP produced by FMTP covers a higher portion of the genome, and uses a smaller number of clones. For instance, on the rice genome the MTP produced by our tool would reduce by about 11 percent the cost of a clone-by-clone sequencing project. Source code, benchmark data sets, and documentation of FMTP are freely available at \u3ehttp://code.google.com/p/fingerprint-based-minimal-tiling-path/ under MIT license

    ProcessDriver: A Computational Pipeline to Identify Copy Number Drivers and Associated Disrupted Biological Processes in Cancer

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    Copy number amplifications and deletions that are recurrent in cancer samples harbor genes that confer a fitness advantage to cancer tumor proliferation and survival. One important challenge in computational biology is to separate the causal (i.e., driver) genes from passenger genes in large, aberrated regions. Many previous studies focus on the genes within the aberration (i.e., cis genes), but do not utilize the genes that are outside of the aberrated region and dysregulated as a result of the aberration (i.e., trans genes). We propose a computational pipeline, called ProcessDriver, that prioritizes candidate drivers by relating cis genes to dysregulated trans genes and biological processes. ProcessDriver is based on the assumption that a driver cis gene should be closely associated with the dysregulated trans genes and biological processes, as opposed to previous studies that assume a driver cis gene should be the most correlated gene to the copy number of an aberrated region. We applied our method on breast, bladder and ovarian cancer data from the Cancer Genome Atlas database. Our results included previously known driver genes and cancer genes, as well as potentially novel driver genes. Additionally, many genes in the final set of drivers were linked to new tumor events after initial treatment using survival analysis. Our results highlight the importance of selecting driver genes based on their widespread downstream effects in trans

    A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

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    DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes

    Genome-Wide Associations of Signaling Pathways in Glioblastoma Multiforme

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    Background: eQTL analysis is a powerful method that allows the identification of causal genomic alterations, providing an explanation of expression changes of single genes. However, genes mediate their biological roles in groups rather than in isolation, prompting us to extend the concept of eQTLs to whole gene pathways. Methods: We combined matched genomic alteration and gene expression data of glioblastoma patients and determined associations between the expression of signaling pathways and genomic copy number alterations with a non-linear machine learning approach. Results: Expectedly, over-expressed pathways were largely associated to tag-loci on chromosomes with signature alterations. Surprisingly, tag-loci that were associated to under-expressed pathways were largely placed on other chromosomes, an observation that held for composite effects between chromosomes as well. Indicating their biological relevance, identified genomic regions were highly enriched with genes having a reported driving role in gliomas. Furthermore, we found pathways that were significantly enriched with such driver genes. Conclusions: Driver genes and their associated pathways may represent a functional core that drive the tumor emergence and govern the signaling apparatus in GBMs. In addition, such associations may be indicative of drug combinations for the treatment of brain tumors that follow similar patterns of common and diverging alterations

    Involvement of MicroRNA Families in Cancer

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    Collecting representative sets of cancer microRNAs (miRs) from the literature we show that their corresponding families are enriched in sets of highly interacting miR families. Targeting cancer genes on a statistically significant level, such cancer miR families strongly intervene with signaling pathways that harbor numerous cancer genes. Clustering miR family-specific profiles of pathway intervention, we found that different miR families share similar interaction patterns. Resembling corresponding patterns of cancer miRs families, such interaction patterns may indicate a miR familyā€™s potential role in cancer. As we find that the number of targeted cancer genes is a naıĀØve proxy for a cancer miR family, we design a simple method to predict candidate miR families based on gene-specific interaction profiles. Assessing the impact of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate families, including 75% of initially collected cancer miR families. Further confirming their relevance, predicted cancer miR families are significantly indicated in increasing, non-random numbers of tumor types

    GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes

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    Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas

    A Semantics for Hyperintensional Belief Revision Based on Information Bases

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    Managing Diverse Online Networks in the Context of Polarization:Understanding how we grow apart on and through social media

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    Social media enable their users to be connected with a diverse group of people increasing their chances of coming across divergent viewpoints. Thus, network diversity is a key issue for understanding the potentials of social media for creating a cross-cutting communication space that is one of the premises of a functioning democracy. This article analyzes the strategies social media users adopt to manage their network diversity in the context of increasing polarization. The study is based on 29 semi-structured interviews with diverse social media users from Turkey and qualitative network maps. Furthermore, the study adopts a cross-platform approach comparing Facebook, Twitter, and WhatsApp in relation to the diversity of their usersā€™ networks. The study shows that social media users adopt different strategies interchangeably in specific contexts. These strategies include visible (unfriending, blocking) and invisible (muting, unfollowing, and ignoring) forms of disconnection, debating, observing divergent opinions, and self-censorship. Political interest of social media users, political climate, issue sensitivity, and ā€œimagined affordancesā€ of social media platforms play a role in usersā€™ choices about which strategy to choose when they are confronted with divergent viewpoints through their diverse online networks. Building on the unfriending literature that points out to rather partisan users, who unfriend, unfollow or block others, this article demonstrates that in peak moments of polarization, also the politically disengaged or moderate users disconnect from diverse others

    Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes

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    Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes

    The genetics of a putative social trait in natural populations of yeast

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    The sharing of secreted invertase by yeast cells is a well established laboratory model for cooperation, but the only evidence that such cooperation occurs in nature is that the SUC loci, which encode invertase, vary in number and functionality. Genotypes that do not produce invertase can act as ā€œcheatsā€ in laboratory experiments, growing on the glucose that is released when invertase producers, or ā€œcooperatorsā€, digest sucrose. However, genetic variation for invertase production might instead be explained by adaptation of different populations to different local availabilities of sucrose, the substrate for invertase. Here we find that, 110 wild yeast strains isolated from natural habitats, all contained a single SUC locus and produced invertase; none were ā€œcheatsā€. The only genetic variants we found were three strains isolated instead from sucrose-rich nectar, which produced higher levels of invertase from three additional SUC loci at their sub-telomeres. We argue that the pattern of SUC gene variation is better explained by local adaptation than by social conflict
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