The influenza virus is one of the most deadly infectious agents known to man and has been responsible for the deaths of some hundred million lives throughout human history. The need to rapidly and reliably survey circ...
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The influenza virus is one of the most deadly infectious agents known to man and has been responsible for the deaths of some hundred million lives throughout human history. The need to rapidly and reliably survey circulating virus strains down to the molecular level is ever present. This tutorial describes the development and application of a new proteotyping approach that harnesses the power of high resolution of mass spectrometry to characterise the influenza virus, and by extension other bacterial and viral pathogens. The approach is shown to be able to type, subtype, and determine the lineage of human influenza virus strains through the detection of one or more signature peptide ions in the mass spectrum of whole virus digests. Pandemic strains can be similarly distinguished from seasonal ones, and new computer algorithms have been written to allow reassorted strains that pose the greatest pandemic risk to be rapidly identified from such datasets. The broader application of the approach is further demonstrated here for the parainfluenza virus, a virus which can be life threatening to children and presents similar clinical symptoms to influenza.
Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, used for the semantic annotation of data, and ontologies are used in structured data formats and databases to avoid in...
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Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, used for the semantic annotation of data, and ontologies are used in structured data formats and databases to avoid inconsistencies in annotation, to have a unique (and preferably short) accession number and to give researchers and computer algorithms the possibility for more expressive semantic annotation of data. The Human Proteome Organization (HUPO)-Proteomics Standards Initiative (PSI) makes extensive use of ontologies/CVs in their data formats. The PSI-Mass Spectrometry (MS) CV contains all the terms used in the PSI MS-related data standards. The CV contains a logical hierarchical structure to ensure ease of maintenance and the development of software that makes use of complex semantics. The CV contains terms required for a complete description of an MS analysis pipeline used in proteomics, including sample labeling, digestion enzymes, instrumentation parts and parameters, software used for identification and quantification of peptides/proteins and the parameters and scores used to determine their significance. Owing to the range of topics covered by the CV, collaborative development across several PSI working groups, including proteomics research groups, instrument manufacturers and software vendors, was necessary. In this article, we describe the overall structure of the CV, the process by which it has been developed and is maintained and the dependencies on other ontologies.
Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple prot...
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Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions' problem.
This study proposes a method to combine the k-Nearest Neighbor (k-NN) algorithm and the Support Vector Machine (SVM) method to increase the image annotation accuracy. Image annotation is widely employed in domains suc...
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This study proposes a method to combine the k-Nearest Neighbor (k-NN) algorithm and the Support Vector Machine (SVM) method to increase the image annotation accuracy. Image annotation is widely employed in domains such as web image classification, search, military, and biomedicine. Although the traditional Border/Interior pixel Classification (BIC) features are very efficient and compact when applied to image annotation to capture color, shape, and texture information, the color space histogram utilization rates are not balanced. The experiment results show that the Hilbert-scan method and the One-pass Partitioning Method (OPM) can effectively overcome the imbalance problem. (C) 2012 Elsevier Ltd. All rights reserved.
Motivation: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing thro...
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Motivation: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results: To align our large (> 80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of > 50 in mapping speed, aligning to the human genome 550 million 2 x 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy.
FTIR micro-spectral images of Caki-2 cells cytospun onto calcium fluoride (CaF2) slides were used to build a computational model in order to discriminate between the biochemical events of the continuous cell cycle dur...
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FTIR micro-spectral images of Caki-2 cells cytospun onto calcium fluoride (CaF2) slides were used to build a computational model in order to discriminate between the biochemical events of the continuous cell cycle during proliferation. Multivariate analysis and machine learning techniques such as PCA, PLSR and SVMs were used to highlight the chemical differences among the cell cycle phases and also to point out the need for removing the distortion of the spectra due to the morphology of the cells. Results showed cell cycle dependant scattering profiles that enabled the training of a SVM in order to recognise, with a relative high accuracy, each cell cycle phase purely with the scattering curve removed from the FTIR data after being subject to the RMieS-EMSC algorithm.
The paper addresses the problem of row straightening of agents via local interactions. A nonlinear control protocol that ensures finite-time equidistant allocation on a segment is proposed. With the designed protocol,...
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The paper addresses the problem of row straightening of agents via local interactions. A nonlinear control protocol that ensures finite-time equidistant allocation on a segment is proposed. With the designed protocol, any settling time can be guaranteed regardless of the initial conditions. A robust modification of the control algorithm based on sliding mode control technique is presented. The case of multidimensional agents is also considered. The theoretical results are illustrated via numerical simulations.
We have developed Cake, a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than an...
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We have developed Cake, a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than any one algorithm alone. Cake can be run on a high-performance computer cluster or used as a stand-alone application.
Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but ...
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Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting;SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers;and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats.
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