20,444 research outputs found

    iSeqQC: a tool for expression-based quality control in RNA sequencing.

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    BACKGROUND: Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers. RESULTS: Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC). CONCLUSION: iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches

    Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

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    The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

    Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

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    New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

    Novel cell models to study breast tumour microenvironment and disease progression

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    Breast cancer is the most prevalent and deadly in woman. ER+ breast cancer represents around two-thirds of all cases and has a favourable prognosis due to good response to endocrine therapy. However, these tumours present 25% of disease relapse due to drug resistance and metastatic behaviour. Tumour progression and acquired drug resistance are modulated by the interactions between tumour cells and the surrounding microenvironment. Most models employed to address these mechanisms fail to reflect the complex tumour microenvironment and do not allow long-term monitoring of tumour progression. (...
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