Bioinformatics
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There have been numerous applications developed for decoding and visualization of ab1 DNA sequencing files for Windows and MAC platforms, yet none exists for the increasingly popular smartphone operating systems. The ability to decode sequencing files cannot easily be carried out using browser accessed Web tools. To overcome this hurdle, we have developed a new native app called DNAApp that can decode and display ab1 sequencing file on Android and iOS. In addition to in-built analysis tools such as reverse complementation, protein translation and searching for specific sequences, we have incorporated convenient functions that would facilitate the harnessing of online Web tools for a full range of analysis. Given the high usage of Android/iOS tablets and smartphones, such bioinformatics apps would raise productivity and facilitate the high demand for analyzing sequencing data in biomedical research. ⋯ samuelg@bii.a-star.edu.sg.
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In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks. ⋯ Supplementary Data are available at Bioinformatics online.
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The generation of large volumes of omics data to conduct exploratory studies has become feasible and is now extensively used to gain new insights in life sciences. The effective exploration of the generated data by experts is a crucial step for the successful extraction of knowledge from these datasets. This requires availability of intuitive and interactive visualization tools that can display complex data. Matrix heatmaps are graphical representations frequently used for the description of complex omics data. Here, we present jHeatmap, a web-based tool that allows interactive matrix heatmap visualization and exploration. It is an adaptable javascript library designed to be embedded by means of basic coding skills into web portals to visualize data matrices as interactive and customizable heatmaps. ⋯ jHeatmap is freely available at the GitHub code repository at https://github.com/jheatmap/jheatmap. Working examples and the documentation may be found at http://jheatmap.github.io/jheatmap.
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In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. ⋯ In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework.
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Easily visualization of complex data features is a necessary step to conduct studies on next-generation sequencing (NGS) data. We developed STAR, an integrated web application that enables online management, visualization and track-based analysis of NGS data. ⋯ STAR browser system is freely available on the web at http://wanglab.ucsd.edu/star/browser and https://github.com/angell1117/STAR-genome-browser.