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检索条件"主题词=Programming language"

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A New Approach for Reflection of Code Modifications to Model in Synchronization of Architecture Design Model and Code 6

A New Approach for Reflection of Code Modifications to Model...

引用

6th International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Pham, Van Cam Radermacher, Ansgar Gerard, Sebastien CEA LIST Lab Model Driven Engn Embedded Syst PC 174 F-91191 Gif Sur Yvette France

ISBN: (纸本)9789897582837

Model-Driven Engineering (MDE) increases the abstraction level, thus facilitates the design of complex systems. It is possible to create an executable system from a model enriched with detailed behavior specifications. But the graphical modeling of some system aspects is likely less efficient compared to writing code in a programming language. For method signatures, textual editing includes a few lines of text, whereas modeling requires the separate addition of methods along with their parameters. Therefore, we propose to develop systems by combining the strength of graphical modeling with programming languages by allowing a developer to make changes in either notation and synchronize the result with the other one, respectively. Synchronization between model and code is already supported by existing tools, but often restricted to structural elements that have a 1-1 mapping. The synchronization of additional modeling aspects from the code, notably component based modeling in UML and behavior in form of state-machines, is not supported by the state-of-the-art. In order to enable this synchronization, it is important to reduce the abstraction gap and assure a 1-1 mapping if possible. Our proposition is to perform the synchronization with an extended programming language that provides additional language elements for some UML elements, notably those that do not already exist in object-oriented programming languages. This extension uses built-in language facilities, in case of C++ templates and preprocessor macros, and a design pattern that adds a shadow implementation.

关键词： UML State Machine Code Generation Change Reflection Programmers Software Architects C plus Java Annotation Processing programming language Component-based Design Source Code Organization Incremental Reverse

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Machine Learning in Untargeted Metabolomics Experiments

Machine Learning in Untargeted Metabolomics Experiments

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作者： Joshua Heinemann

Machine learning is a form of artificial intelligence (AI) that provides computers with the ability to learn generally without being explicitly programmed. Machine learning refers to the ability of computer programs to adapt when exposed to new data. Here we examine the use of machine learning for use with untargeted metabolomics data, when it is appropriate to use, and questions it can answer. We provide an example workflow for training and testing a simple binary classifier, a multiclass classifier and a support vector machine using the Waikato Environment for Knowledge Analysis (Weka), a toolkit for machine learning. This workflow should provide a framework for greater integration of machine learning with metabolomics study. less

关键词： Machine Learning Metabolomics Artificial Intelligence Statistical Calculation Decision Tree Mass Spectrometry programming language Data analysis Supervised learning

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Single-Cell Transcriptome Analysis of T Cells

Single-Cell Transcriptome Analysis of T Cells

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作者： Willem Van Der Byl Simone Rizzetto Jerome Samir Curtis Cai Auda A. Eltahla Fabio Luciani

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology. less

关键词： Single-cell RNA-seq Library Preparation Flow Cytometry PCR Bowtie RNA Extraction Principal Component Analysis Differential Expression Analysis Gene Expression Analysis Trimmomatic Ensembl Python programming language Hepatitis C virus T cells Alignment Clustering Differential gene expression T cell receptor reconstruction Gene expression matrix

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Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Creation and analysis of biochemical constraint-based models...

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作者： Laurent Heirendt Sylvain Arreckx Thomas Pfau Sebastián N. Mendoza Anne Richelle Almut Heinken Hulda S. Haraldsdóttir Jacek Wachowiak Sarah M. Keating Vanja Vlasov Stefania Magnusdóttir Chiam Yu Ng German Preciat Alise Žagare Siu H. Chan Maike K. Aurich Catherine M. Clancy Jennifer Modamio John T. Sauls Alberto Noronha Aarash Bordbar Benjamin Cousins Diana C. El Assal Luis V. Valcarcel Iñigo Apaolaza Susan Ghaderi Masoud Ahookhosh Marouen Guebila Andrejs Kostromins Nicolas Sompairac Hoai M. Le Ding Ma Yuekai Sun Lin Wang James T. Yurkovich Miguel A. Oliveira Phan T. Vuong Lemmer P. El Assal Inna Kuperstein Andrei Zinovyev H. Scott Hinton William A. Bryant Francisco J. Aragón Artacho Francisco J. Planes Egils Stalidzans Alejandro Maass Santosh Vempala Michael Hucka Michael A. Saunders Costas D. Maranas Nathan E. Lewis Thomas Sauter Bernhard Ø. Palsson Ines Thiele Ronan M. Fleming

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. less

关键词： Metabolomics Pathway Or Network Visualization MATLAB Statistical Calculation Image Processing Machine Learning programming language Python Computational models Software

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Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Probabilistic cell-type assignment of single-cell RNA-seq fo...

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作者： Allen W. Zhang Ciara O’Flanagan Elizabeth A. Chavez Jamie L. Lim Nicholas Ceglia Andrew McPherson Matt Wiens Pascale Walters Tim Chan Brittany Hewitson Daniel Lai Anja Mottok Clementine Sarkozy Lauren Chong Tomohiro Aoki Xuehai Wang Andrew P Weng Jessica N. McAlpine Samuel Aparicio Christian Steidl Kieran R. Campbell Sohrab P. Shah

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.

关键词： Genomics Single-cell RNA-seq Statistical Inference Differential Expression Analysis Cell And Tissue Culture Cell Enrichment Library Preparation Principal Component Analysis Immunohistochemistry Seurat Data analysis programming language Computational models Statistical methods Cancer microenvironment

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Identification of Genomic Alterations Through Multilevel DNA Structural Analysis

Identification of Genomic Alterations Through Multilevel DNA...

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作者： Ryan K. Shultzaberger John Dresios

Current methods to identify genomic alterations using whole-genome sequencing (WGS) data are often limited to single nucleotide polymorphismsSingle nucleotide polymorphism (SNP) and insertions and deletions that are less than 10 bp in length. These limitations are largely due to challenges in accurately mapping short sequencing reads that significantly diverge from the reference genome. Newer sequencing-based methods have been developed to define and characterize larger DNA structural elements. This is achieved by enriching for and sequencing regions of the genome that contain a specific element, followed by identifying genomic regions with high densities of mapped short reads that designate the location of these elements. This process essentially aggregates short read data into larger structural units for further characterization. Here, we describe protocols for identifying various types of genomic alterations using differential analysis of these structural units. We focus on changes in DNA accessibility, protein–DNA interactions, and chromosomal contacts as measured by ATAC-Seq, ChIP-Seq, and Hi-C respectively. As many protocols have been published describing the generation and processing of these data, we focus on simple methods that can be used to identify mutations in these data, and can be executed by someone with limited computational expertise. less

关键词： ChIP-seq ATAC-Seq Whole-genome Sequencing Immunoprecipitation Genetic Engineering Genomics PCR Sanger Sequencing programming language Genome Sequencing Chromatin Hi-C Mutation detection DNA–protein interactions DNA organization DNA accessibility

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Improving miRNA Target Prediction Using CLASH Data

Improving miRNA Target Prediction Using CLASH Data

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作者： Xiaoman Li Haiyan Hu

In this chapter, we present a computational method, TarPmiR, for miRNA target prediction. TarPmiR is based on emerging features of miRNA–target interactions learned from CLASH (crosslinking, ligation and sequencing of hybrids) data. First, we introduce miRNA target prediction, delineate existing methods for miRNA target prediction, and discuss their usage and limitations. Next, we describe available CLASH data, the learning of new miRNA binding features from CLASH data, and the usage of CLASH features in miRNA target prediction. Finally, we detail the computational pipeline of TarPmiR, discuss its performance compared with existing computational methods for miRNA target prediction, and present its installation and usage for miRNA target prediction. This chapter will facilitate the common understanding of CLASH data, new characteristics of miRNA–target interactions, and the use of the CLASH based miRNA target prediction tool TarPmiR. less

关键词： Machine Learning High-Throughput Sequencing CLIP-Seq PAR-CLIP Python programming language miRNA miRNA target prediction CLASH data New features TarPmiR

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RNA Sequencing in B-Cell Lymphomas

RNA Sequencing in B-Cell Lymphomas

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作者： Da Wei Huang Moez Dawood Calvin A. Johnson Roland Schmitz

High-throughput mRNA sequencing (RNA-Seq) provides both qualitative and quantitative evaluation of the transcriptome. This method uses complementary DNA (cDNA) to generate several millions of short sequence reads that are aligned to a reference genome allowing the comprehensive characterization of the transcripts in a cell. RNA-Seq has a wide variety of applications which lead to a pervasive adoption of this method well beyond the genomics community and a deployment of this technique as a standard part of the toolkit applied in life sciences. This chapter describes a protocol to perform mRNA sequencing using the Illumina NextSeq or MiSeq platforms, presents sequencing data quality metrics, and outlines a bioinformatic pipeline for sequence alignment, digital gene expression, identification of gene fusions, detection of transcript isoforms, description and annotation of genetic variants, and de novo immunoglobulin gene assembly. less

关键词： High-Throughput Sequencing RNA-seq Library Preparation Real-time PCR Genomics RNA Purification Nucleic Acid Microarray Image Analysis Gene Expression Analysis Microarray RNA-Seq Analysis Data Mining Cufflinks Genome Alignment Variant Calling Rearrangement Trinity mRNA sequencing DNA Ligation Data analysis Trimmomatic SAMtools programming language TruSeq Rattus norvegicus Gene expression Mutation B cell B-cell lymphoma Transcriptome Immunoglobulin genes VDJ

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The Fundamentals of Constructing and Interpreting Heat Maps

The Fundamentals of Constructing and Interpreting Heat Maps

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作者： Nathaniel M. Vacanti

This chapter is intended to introduce the fundamental principles of the heat map, the most widely used medium to present high-throughput data, to scientists unaccustomed to analyzing large data sets. Its scope includes describing the general features of heat maps, how their components are designed, the meaning of parameters such as “distance method” and “linkage method,” and the influence of manipulations such as row-scaling and logarithmic transformations on data interpretation and presentation. This chapter may serve as a guide to understanding the use of heat maps in published analyses or to aid in their design, allowing efficient interpretations of high-throughput experiments, exploration of hypotheses, or clear communications of findings. less

关键词： Metabolomics Proteomics RNA-seq programming language Correlation Hierarchical clustering Heat map Dendrogram Linkage method

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Pipeline for Integrated Microarray Expression Normalization Tool Kit (PIMENTo) for Tumor Microarray Profiling Experiments

Pipeline for Integrated Microarray Expression Normalization ...

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作者： Thomas Nash Matthew Huff W. Bailey Glen Gary Hardiman

We have developed a Pipeline for Integrated Microarray Expression & Normalization Tool kit (PIMENTo) with the aim of streamlining the processes necessary for gene expression analysis in tumor tissue using DNA microarrays. Built with the R programming language and leveraging several open-source packages available through CRAN and Bioconductor, PIMENTo enables researchers to perform complex tasks with a minimal number of operations. Here, we describe the pipeline, review necessary data inputs, examine data outputs and quality control assessments and explore the commands to perform such analysis. less

关键词： Microarray Gene Expression Analysis Principal Component Analysis programming language Mus musculus R Quality control Normalization Tumor Genomic profiling Illumina Bead arrays

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