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 f...
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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
The goal is to find a universal model of a data source for accessing heterogeneous data spaces and heterogeneous types of information on the Internet/web. The key area of data sources of today's world are database...
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ISBN:
(纸本)9789549641387
The goal is to find a universal model of a data source for accessing heterogeneous data spaces and heterogeneous types of information on the Internet/web. The key area of data sources of today's world are databases. However, to access them is very difficult due to the variety of their nature and the inconsistent approach to data and their accessing. That is why a well created model of a data source will enable an easy and mainly consistent approach to the data sources both saved in the classic (relational) or specialized databases (object, XML, etc.), and other data sources. It is the interlink between the data source and the user (man, computer), which will remove the inconsistency of accessing data and will secure a user-friendly tool for cooperating with the data source. The formalism of the model will include also nonstandard methods of search, such as general search, contextual search, search for non-textual information.
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 ...
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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
We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy...
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We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy modalities. The method is based on the iterative optimization of Voronoi representations of the spatial structures. The reconstructions of biological surfaces automatically adapt to morphological features of varying complexity with flexible degrees of resolution. We show how 3-D confocal images of single cells can be used to generate numerical representations of cellular membranes that may serve as the basis for realistic, spatially resolved computational models of membrane processes or intracellular signaling. Another example shows how the protocol can be used to reconstruct tissue boundaries from segmented two-photon image data that facilitate the quantitative analysis of lymphocyte migration behavior in relation to microanatomical structures. Processing time is of the order of minutes depending on data features and reconstruction parameters. less
In this methods article, I describe a computational workflow for cross-species visualization and comparison of mRNA-seq transcriptome profiling data. The workflow is based on gene set variation analysis (GSVA) and is ...
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In this methods article, I describe a computational workflow for cross-species visualization and comparison of mRNA-seq transcriptome profiling data. The workflow is based on gene set variation analysis (GSVA) and is illustrated using commands in the R programming language. I provide a complete step-by-step procedure for the workflow using mRNA-seq data sets from dog and human bladder cancer as an example. less
EXtensible Markup language (XML) technology provides an ideal representation for the complex structure of models and neuroscience data, as it is an open file format and provides a language-independent method for stori...
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EXtensible Markup language (XML) technology provides an ideal representation for the complex structure of models and neuroscience data, as it is an open file format and provides a language-independent method for storing arbitrarily complex structured information. XML is composed of text and tags that explicitly describe the structure and semantics of the content of the document. In this chapter, we describe some of the common uses of XML in neuroscience, with case studies in representing neuroscience data and defining model descriptions based on examples from NeuroML. The specific methods that we discuss include (1) reading and writing XML from applications, (2) exporting XML from databases, (3) using XML standards to represent neuronal morphology data, (4) using XML to represent experimental metadata, and (5) creating new XML specifications for models. less
In vivo peptide-phage display is an unbiased technique for mapping of the vascular diversity and identification of homing peptides. This chapter is intended to serve as a structured practical guide to execute in vivo ...
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In vivo peptide-phage display is an unbiased technique for mapping of the vascular diversity and identification of homing peptides. This chapter is intended to serve as a structured practical guide to execute in vivo T7 phage biopanning and data analysis experiments. We discuss experimental designs and protocols with emphasis on application of high-throughput sequencing-based technologies for streamlined in vivo biopanning and validation of homing peptides. less
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 ma...
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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.
The development of open source software has gained popularity. Most of the software projects use diverse sets of programming languages for development. In this work, the Knowledge Discovery in Data (KDD) approach to a...
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ISBN:
(纸本)9781509041725
The development of open source software has gained popularity. Most of the software projects use diverse sets of programming languages for development. In this work, the Knowledge Discovery in Data (KDD) approach to analyze the data of 30,518 open source projects hosted on SourceForge. The process of knowledge discovery is explored by using the association rule mining algorithm to find the programming languages, which are often used together in combination for the development of software project. The group-matrix based visualization technique is further implied to visualize the generated associated group of languages. The generated knowledge base and visualization of associated languages provide current and future developers with insight knowledge of multiple set of programming languages which are used together frequently for the development of open source software projects.
Whole-genome sequencing with short-read technologies is well suited for calling single nucleotide polymorphisms, but has major problems with the detection of structural variants larger than the read length. One such t...
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Whole-genome sequencing with short-read technologies is well suited for calling single nucleotide polymorphisms, but has major problems with the detection of structural variants larger than the read length. One such type of variation is copy number variation (CNV), which entails deletion or duplication of genomic regions, and the expansion or contraction of repeated elements. Duplicated and deleted regions will typically be collapsed during de novo assembly of sequence data, or ignored when mapping reads toward a reference. However, signatures of the copy number variation can be detected in the resultant read depth at each position in the genome. We here provide instructions on how to analyze this read depth signal with the R package CNOGpro, allowing for estimation of copy numbers with uncertainty for each feature in a genome. less
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