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检索条件"主题词=Programming Language"

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Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery

Supervised Machine Learning with CITRUS for Single Cell Biom...

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作者： Hannah G. Polikowsky Katherine A. Drake

CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated with an outcome. The algorithm is a black box that includes steps to cluster cell populations, characterize these populations, and identify the significant characteristics. This chapter describes how to optimize the use of CITRUS by combining it with upstream and downstream data analysis and visualization tools. less

关键词： Machine Learning Mass Cytometry Data analysis programming language Biomarker discovery viSNE CITRUS anti-bcr

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Objective comparison of particle tracking methods

Objective comparison of particle tracking methods

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作者： Nicolas Chenouard Ihor Smal Fabrice de Chaumont Martin Maška Ivo F Sbalzarini Yuanhao Gong Janick Cardinale Craig Carthel Stefano Coraluppi Mark Winter Andrew R Cohen William J Godinez Karl Rohr Yannis Kalaidzidis Liang Liang James Duncan Hongying Shen Yingke Xu Klas E G Magnusson Joakim Jaldén Helen M Blau Perrine Paul-Gilloteaux Philippe Roudot Charles Kervrann François Waharte Jean-Yves Tinevez Spencer L Shorte Joost Willemse Katherine Celler Gilles P van Wezel Han-Wei Dan Yuh-Show Tsai Carlos Ortiz de Solórzano Jean-Christophe Olivo-Marin Erik Meijering

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers. less

关键词： Fluorescence Microscopy Image Processing Microscopy Time Lapse Microscopy programming language

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Global Quantitative Proteomics Using Spectral Counting: An Inexpensive Experimental and Bioinformatics Workflow for Deep Proteome Coverage

Global Quantitative Proteomics Using Spectral Counting: An I...

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作者： Tiago S. Balbuena Diogo Ribeiro Demartini Jay J. Thelen

As the field of proteomics shifts from qualitative identification of protein “subfractions” to quantitative comparison of proteins from complex biological samples, it is apparent that the number of approaches for quantitation can be daunting for the result-oriented biologist. There have been many recent reviews on quantitative proteomic approaches, discussing the strengths and limitations of each. Unfortunately, there are few detailed methodological descriptions of any one of these quantitative approaches. Here we present a detailed bioinformatics workflow for one of the simplest, most pervasive quantitative approach—spectral counting. The informatics and statistical workflow detailed here includes newly available freeware, such as SePro and PatternLab which post-process data according to false discovery rate parameters, and statistically model the data to detect differences and trends. less

关键词： Multidimensional Protein Identification Technology Proteomics Spectral Counting Statistical Calculation programming language GeLC

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Mass Balance Imaging: A Phase Portrait Analysis for Characterizing Growth Kinetics of Biomolecular Condensates

Mass Balance Imaging: A Phase Portrait Analysis for Characte...

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作者： Jan Geisler Victoria Tianjing Yan Stephan Grill Arjun Narayanan

Biomolecular condensation has emerged as a key organizing principle governing the formation of membraneless cellular assemblies. Revealing the mechanism of formation of biomolecular condensates requires the quantitative examination of their growth kinetics. Here, we introduce mass balance imaging (MBI) as a general method to study compositional growth dynamics based on fluorescent images of multicomponent clusters. MBI allows the visualization and measurement of composition-dependent growth rates of biomolecular condensates and other assemblies. We provide a computational pipeline and demonstrate the applicability of our method by investigating cortical assemblies containing N-WASP (WSP-1) and F-actin that appear during oocyte cortex activation in C. elegans. In general, the method can be broadly implemented to identify interactions that underlie growth kinetics of multicomponent assemblies in vivo and in vitro. less

关键词： Image Analysis Fiji Deep learning Python Fluorescence Microscopy programming language Caenorhabditis elegans Colocalization Phase separation Growth kinetics Biomolecular condensates Membraneless organelles Mass balance imaging Multicomponent clustering Phase portrait analysis

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Statistical and Multivariate Analysis of MS-Based Plant Metabolomics Data

Statistical and Multivariate Analysis of MS-Based Plant Meta...

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作者： Carsten Jaeger Jan Lisec

Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification. less

关键词： Metabolomics Statistical Calculation Principal Component Analysis Gas Chromatography-mass Spectrometry Data analysis Python programming language Quality control Data normalization Bioinformatics tools R programing language R software packages

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Single Marker Family-Based Association Analysis Not Conditional on Parental Information

Single Marker Family-Based Association Analysis Not Conditio...

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作者： Junghyun Namkung Sungho Won

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, we review popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE), the mixed model with a polygenic random component, and genome-wide association analyses. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. Genome-wide association studies usually test more than 10,000 SNPs and thus traditional statistical methods accounting for the familial correlations often suffer from a computational burden. Multiple approaches that have been recently proposed to avoid this computational issue are reviewed. The single-marker analysis procedures are demonstrated using the R package gee and the ASSOC program in the S.A.G.E. package, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data. less

关键词： Statistical Calculation programming language Genome-wide association studies GWAS Heritability S.A.G.E. Family-based association test Unconditional method Observed genotype Polygenic inheritance Linear mixed model Marginal model Generalized estimating equations GEE Generalized linear mixed model Variance components ASSOC R package gee Working correlation

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Using GenBank and SRA

Using GenBank and SRA

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作者： Eric W. Sayers Chris O’Sullivan Ilene Karsch-Mizrachi

GenBank® and the Sequence Read Archive (SRA) are comprehensive databases of publicly available DNA sequences. GenBank contains data for 480,000 named organisms, more than 176,000 within the embryophyta, obtained through submissions from individual laboratories and batch submissions from large-scale sequencing projects. SRA contains reads from next-generation sequencing studies from over 110,000 species. Daily data exchange with the European Nucleotide Archive (ENA) in Europe and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage for both databases. GenBank and SRA data are accessible through the NCBI Entrez retrieval system that integrates these data with other data at NCBI, such as genomes, taxonomy, and the biomedical literature. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Usage scenarios for both GenBank and SRA ranging from local and cloud analyses to online analyses supported by the NCBI web-based tools are discussed. Both GenBank and SRA, along with their related retrieval and analysis services, are available from the NCBI homepage at *** . less

关键词： Genome Assembly High-Throughput Sequencing Statistical Calculation GenBank Genome Sequencing programming language Zea mays Nicotiana tabacum Arabidopsis thaliana Oryza sativa Japonica Group Populus trichocarpa Solanum lycopersicum Triticum aestivum Allium Glycine max Pisum sativum BLAST DNA sequence SRA NCBI

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Modeling Community Population Dynamics with the Open-Source language R

Modeling Community Population Dynamics with the Open-Source ...

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作者： Robin Green Wenying Shou

The ability to explain biological phenomena with mathematics and to generate predictions from mathematical models is critical for understanding and controlling natural systems. Concurrently, the rise in open-source software has greatly increased the ease at which researchers can implement their own mathematical models. With a reasonably sound understanding of mathematics and programming skills, a researcher can quickly and easily use such tools for their own work. The purpose of this chapter is to expose the reader to one such tool, the open-source programming language R, and to demonstrate its practical application to studying population dynamics. We use the Lotka–Volterra predator–prey dynamics as an *** ability to explain biological phenomena with mathematics and to generate predictions from mathematical models is critical for understanding and controlling natural systems. Concurrently, the rise in open-source software has greatly increased the ease at which researchers can implement their own mathematical models. With a reasonably sound understanding of mathematics and programming skills, a researcher can quickly and easily use such tools for their own work. The purpose of this chapter is to expose the reader to one such tool, the open-source programming language R, and to demonstrate its practical application to studying population dynamics. We use the Lotka–Volterra predator–prey dynamics as an example.

关键词： programming language R Modeling Population dynamics Lotka–Volterra Predator–prey relationship

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Analysis of High-Throughput Ancient DNA Sequencing Data

Analysis of High-Throughput Ancient DNA Sequencing Data

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作者： Martin Kircher

Advances in sequencing technologies have dramatically changed the field of ancient DNA (aDNA). It is now possible to generate an enormous quantity of aDNA sequence data both rapidly and inexpensively. As aDNA sequences are generally short in length, damaged, and at low copy number relative to coextracted environmental DNA, high-throughput approaches offer a tremendous advantage over traditional sequencing approaches in that they enable a complete characterization of an aDNA extract. However, the particular qualities of aDNA also present specific limitations that require careful consideration in data analysis. For example, results of high-throughout analyses of aDNA libraries may include chimeric sequences, sequencing error and artifacts, damage, and alignment ambiguities due to the short read lengths. Here, I describe typical primary data analysis workflows for high-throughput aDNA sequencing experiments, including (1) separation of individual samples in multiplex experiments; (2) removal of protocol-specific library artifacts; (3) trimming adapter sequences and merging paired-end sequencing data; (4) base quality score filtering or quality score propagation during data analysis; (5) identification of endogenous molecules from an environmental background; (6) quantification of contamination from other DNA sources; and (7) removal of clonal amplification products or the compilation of a consensus from clonal amplification products, and their exploitation for estimation of library complexity. less

关键词： High-Throughput Sequencing DNA Sequencing PCR Sample Pooling Library Preparation Sequence Annotation Statistical Calculation Data analysis Python programming language Genome Analysis Next-generation sequencing Adapters Contamination Ancient DNA Quality scores 454 Illumina/Solexa SOLiD-barcode Sample index Chimeric sequences Endogenous DNA

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Discovery of RNA Motifs Using a Computational Pipeline that Allows Insertions in Paired Regions and Filtering of Candidate Sequences

Discovery of RNA Motifs Using a Computational Pipeline that ...

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作者： Randi M. Jimenez Ladislav Rampášek Broňa Brejová Tomáš Vinař Andrej Lupták

The enormous impact of noncoding RNAs on biology and biotechnology has motivated the development of systematic approaches to their discovery and characterization. Here we present a methodology for reliable detection of genomic ribozymes that centers on pipelined structure-based searches, utilizing two versatile algorithms for structure prediction. RNArobo is a prototype structure-based search package that enables a single search to return all sequences matching a designated motif descriptor, taking into account the possibility of single nucleotide insertions within base-paired regions. These outputs are then filtered through a structure prediction algorithm based on free energy minimization in order to maximize the proportion of catalytically active RNA motifs. This pipeline provides a fast approach to uncovering new catalytic RNAs with known secondary structures and verifying their activity in *** enormous impact of noncoding RNAs on biology and biotechnology has motivated the development of systematic approaches to their discovery and characterization. Here we present a methodology for reliable detection of genomic ribozymes that centers on pipelined structure-based searches, utilizing two versatile algorithms for structure prediction. RNArobo is a prototype structure-based search package that enables a single search to return all sequences matching a designated motif descriptor, taking into account the possibility of single nucleotide insertions within base-paired regions. These outputs are then filtered through a structure prediction algorithm based on free energy minimization in order to maximize the proportion of catalytically active RNA motifs. This pipeline provides a fast approach to uncovering new catalytic RNAs with known secondary structures and verifying their activity in vitro.

关键词： Structure Prediction NCBI BLAST Multiple Sequence Alignment programming language Python Ribozymes Pseudoknots RNABOB RNArobo RNAfold

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