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检索条件"主题词=Programming Language"

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Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria

Bioinformatic Approach for Prediction of CsrA/RsmA-Regulatin...

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作者： Carl T. Fakhry Kourosh Zarringhalam Rahul V. Kulkarni

CsrA/RsmA is a RNA-binding protein that functions as a global regulator controlling important processes such as virulence, secondary metabolism, motility, and biofilm formation in diverse bacterial species. The activity of CsrA/RsmA is regulated by small RNAs that contain multiple binding sites for the protein. The expression of these noncoding RNAs effectively sequesters the protein and reduces free cellular levels of CsrA/RsmA. While multiple bacterial small RNAs that bind to and regulate CsrA/RsmA levels have been discovered, it is anticipated that there are several such small RNAs that remain undiscovered. To assist in the discovery of these small RNAs, we have developed a bioinformatics approach that combines sequence- and structure-based features to predict small RNA regulators of CsrA/RsmA. This approach analyzes structural motifs in the ensemble of low energy secondary structures of known small RNA regulators of CsrA/RsmA and trains a binary classifier on these features. The proposed machine learning approach leads to several testable predictions for small RNA regulators of CsrA/RsmA, thereby complementing and accelerating experimental efforts aimed at discovery of noncoding RNAs in the CsrA/RsmA pathway. less

关键词： Machine Learning programming language Sequence Analysis Tools Small RNAs Noncoding RNA Post-transcriptional regulation CsrA RsmA Computational predictions

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Single-Response Electronic Tongue and Machine Learning Enable the Multidetermination of Extracellular Vesicle Biomarkers for Cancer Diagnostics Without Recognition Elements

Single-Response Electronic Tongue and Machine Learning Enabl...

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作者： Caroline Y. Nicoliche Giulia S. Silva Leticia Gomes-de-Pontes Gabriel R. Schleder Renato S. Lima

Platforms based on impedimetric electronic tongue (nonselective sensor) and machine learning are promising to bring disease screening biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point-of-care, thus contributing to rationalize and decentralize laboratory tests with social and economic impacts being achieved. By combining a low-cost and scalable electronic tongue with machine learning, in this chapter, we describe the simultaneous determination of two extracellular vesicle (EV) biomarkers, i.e., the concentrations of EV and carried proteins, in mice blood with Ehrlich tumor from a single impedance spectrum without using biorecognizing elements. This tumor shows primary features of mammary tumor cells. Pencil HB core electrodes are integrated into polydimethylsiloxane (PDMS) microfluidic chip. The platform shows the highest throughput in comparison with the methods addressed in the literature to determine EV biomarkers. less

关键词： Artificial Intelligence Machine Learning Electroanalytical Method Exclusion Chromatography Extrusion Principal Component Analysis Python programming language Breast cancer Accuracy Point-of-care Impedance Nonselective sensor

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Computational Biology Methods for Characterization of Pluripotent Cells

Computational Biology Methods for Characterization of Plurip...

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作者： Marcos J. Araúzo-Bravo

Pluripotent cells are a powerful tool for regenerative medicine and drug discovery. Several techniques have been developed to induce pluripotency, or to extract pluripotent cells from different tissues and biological fluids. However, the characterization of pluripotency requires tedious, expensive, time-consuming, and not always reliable wet-lab experiments; thus, an easy, standard quality-control protocol of pluripotency assessment remains to be established. Here to help comes the use of high-throughput techniques, and in particular, the employment of gene expression microarrays, which has become a complementary technique for cellular characterization. Research has shown that the transcriptomics comparison with an Embryonic Stem Cell (ESC) of reference is a good approach to assess the pluripotency. Under the premise that the best protocol is a computer software source code, here I propose and explain line by line a software protocol coded in R-Bioconductor for pluripotency assessment based on the comparison of transcriptomics data of pluripotent cells with an ESC of reference. I provide advice for experimental design, warning about possible pitfalls, and guides for results interpretation. less

关键词： Transcriptomics Principal Component Analysis Statistical Calculation Next Generation Sequencing MATLAB Microarray programming language Data analysis Mus musculus R iPSC Cellular reprogramming Computational biology Bioconductor Hierarchical clustering Scatterplots

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Metabolomic Bioinformatic Analysis

Metabolomic Bioinformatic Analysis

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作者： Allyson L. Dailey

Metabolomics allows for the investigation of the small molecules found within living systems. Based on the design of the experiments, it is not uncommon for these analyses to include matrices of thousands of variables. In order to handle such large datasets, many have turned to multivariate statistical analyses to analyze and understand their data. Herein, we present protocols for using R to analyze metabolomic data using some of the more common multivariate statistical techniques. less

关键词： Metabolomics Principal Component Analysis Data analysis programming language GraphPad Prism PCA Correlation network Dendrograms Pheatmap Qgraph Rgl

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Compilation of Custom Compound/Bioactivity Datasets from Public Repositories

Compilation of Custom Compound/Bioactivity Datasets from Pub...

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作者： Laura Isigkeit Daniel Merk

Public repositories containing compound–bioactivity data for millions of small molecules offer a valuable resource for chemogenomic compound candidate search. Nonetheless, owning to nonuniform data mining, these databases are often incomplete, thus advocating the combined use of data from several repositories to increase target coverage and data accuracy. Here, we present a workflow to generate custom datasets from public databases for mining chemogenomic compound candidates. The compiled set provides flags for differences in structural and bioactivity data and enables rapid extraction of potent and selective bioactive *** repositories containing compound–bioactivity data for millions of small molecules offer a valuable resource for chemogenomic compound candidate search. Nonetheless, owning to nonuniform data mining, these databases are often incomplete, thus advocating the combined use of data from several repositories to increase target coverage and data accuracy. Here, we present a workflow to generate custom datasets from public databases for mining chemogenomic compound candidates. The compiled set provides flags for differences in structural and bioactivity data and enables rapid extraction of potent and selective bioactive compounds.

关键词： Data Mining KNIME Python programming language Ensembl Homo sapiens ChEMBL Big data Data curation Public databases Compound selectivity

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Summarizing RNA-Seq Data or Differentially Expressed Genes Using Gene Set, Network, or Pathway Analysis

Summarizing RNA-Seq Data or Differentially Expressed Genes U...

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作者： Enrica Calura Paolo Martini

The main purpose of pathway or gene set analysis methods is to provide mechanistic insight into the large amount of data produced in high-throughput studies. These tools were developed for gene expression analyses, but they have been rapidly adopted by other high-throughput techniques, becoming one of the foremost tools of omics research. Currently, according to different biological questions and data, we can choose among a vast plethora of methods and databases. Here we use two published examples of RNAseq datasets to approach multiple analyses of gene sets, networks and pathways using freely available and frequently updated software. Finally, we conclude this chapter by presenting a survival pathway analysis of a multiomics dataset. During this overview of different methods, we focus on visualization, which is a fundamental but challenging step in this computational field. less

关键词： RNA-seq Cross-linking Differential Expression Analysis Cytoscape Statistical Calculation Microarray EnrichmentMap programming language Homo sapiens Mus musculus Saccharomyces cerevisiae Rattus norvegicus Networks Pathway Gene set analysis Topological pathway analysis Multiomics data integration

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Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomics Data

Ensembl Plants: Integrating Tools for Visualizing, Mining, a...

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作者： Dan Bolser Daniel M. Staines Emily Pritchard Paul Kersey

Ensembl Plants (http://*** ) is an integrative resource presenting genome-scale information for a growing number of sequenced plant species (currently 33). Data provided includes genome sequence, gene models, functional annotation, and polymorphic loci. Various additional information are provided for variation data, including population structure, individual genotypes, linkage, and phenotype data. In each release, comparative analyses are performed on whole genome and protein sequences, and genome alignments and gene trees are made available that show the implied evolutionary history of each gene family. Access to the data is provided through a genome browser incorporating many specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These access routes are consistent with those offered through the Ensembl interface for the genomes of non-plant species, including those of plant pathogens, pests, and pollinators. Ensembl Plants is updated 4–5 times a year and is developed in collaboration with our international partners in the Gramene (http://*** ) and transPLANT projects (http://*** ). less

关键词： Genomics Transcriptomics Data Mining Genome Visualization RNA-seq Multiple Sequence Alignment Genome Assembly Genome Alignment Ensembl Gramene Database Management System programming language Arabidopsis lyrata Arabidopsis thaliana Chlamydomonas reinhardtii Zea mays Medicago truncatula Setaria italica Physcomitrella patens Populus trichocarpa Populus (cottonwood) Oryza brachyantha Oryza glaberrima Oryza sativa Indica Group Oryza sativa Japonica Group Selaginella moellendorffii Solanum lycopersicum Oryza barthii Oryza punctata Oryza nivara Oryza rufipogon Brassica rapa Solanum tuberosum Prunus Rosaceae Glycine max Triticum aestivum Databases Genome Cereals Phenotype Genetic Crops Comparative genomic Functional genomic

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Web-Based Analysis of (Epi-) Genome Data Using EpiGRAPH and Galaxy

Web-Based Analysis of (Epi-) Genome Data Using EpiGRAPH and ...

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作者： Christoph Bock Greg Kuster Konstantin Halachev James Taylor Anton Nekrutenko Thomas Lengauer

Modern life sciences are becoming increasingly data intensive, posing a significant challenge for most researchers and shifting the bottleneck of scientific discovery from data generation to data analysis. As a result, progress in genome research is increasingly impeded by bioinformatic hurdles. A new generation of powerful and easy-to-use genome analysis tools has been developed to address this issue, enabling biologists to perform complex bioinformatic analyses online - without having to learn a programming language or downloading and manually processing large datasets. In this tutorial paper, we describe the use of EpiGRAPH (http://***/) and Galaxy (http://***/) for genome and epigenome analysis, and we illustrate how these two web services work together to identify epigenetic modifications that are characteristics of highly polymorphic (SNP-rich) promoters. This paper is supplemented with video tutorials (http://***/yc5xkqq), which provide a step-by-step guide through each example analysis. less

关键词： Statistical Calculation Machine Learning Genomics Data Mining Genome Visualization Genome Annotation ChIP-on-chip ChIP-seq Methylation Analysis Bisulfite Sequencing Tag Mapping Genome Assembly Bowtie methylated DNA immunoprecipitation microarray (MeDIP-chip) Genome Analysis Data analysis programming language Microarray Python Ensembl Malus domestica Bioinformatics Single nucleotide polymorphisms (SNPs) Computational epigenetics Evolutionary constraint

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Light-Induced Transcription Activation for Time-Lapse Microscopy Experiments in Living Cells

Light-Induced Transcription Activation for Time-Lapse Micros...

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作者： Jorge Trojanowski Anne Rademacher Fabian Erdel Karsten Rippe

Gene expression can be monitored in living cells via the binding of fluorescently tagged proteins to RNA repeats engineered into a reporter transcript. This approach makes it possible to trace temporal changes of RNA production in real time in living cells to dissect transcription regulation. For a mechanistic analysis of the underlying activation process, it is essential to induce gene expression with high accuracy. Here, we describe how this can be accomplished with an optogenetic approach termed blue light-induced chromatin recruitment (BLInCR). It employs the recruitment of an activator protein to a target promoter via the interaction between the PHR and CIBN plant protein domains. This process occurs within seconds after setting the light trigger and is reversible. Protocols for continuous activation as well as pulsed activation and reactivation with imaging either by laser scanning confocal microscopy or automated widefield microscopy are provided. For the semiautomated quantification of the resulting image series, an approach has been implemented in a set of scripts in the R programming language. Thus, the complete workflow of the BLInCR method is described for mechanistic studies of the transcription activation process as well as the persistence and memory of the activated state. less

关键词： Optogenetics Cell And Tissue Culture Image Analysis Microscopy Time Lapse Microscopy Confocal Microscopy Transfection Fiji ImageJ programming language Image quantification Transcription dynamics

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Fundamental Bioinformatic and Chemoinformatic Data Processing

Fundamental Bioinformatic and Chemoinformatic Data Processin...

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作者： J. B. Brown

In order to execute more advanced computational chemogenomic workflows, it is essential to understand the basic data formats and options for processing them. In this chapter, de facto standards for compound and protein representation are explained, with procedures for processing them given. A walkthrough demonstrates the step-by-step processes of downloading a ligand–target database, parsing the bioactivity in the database, automatically retrieving its chemical structures and protein sequences from a command line, and finally converting the structures and sequences into representative machine-ready formats. A basic protocol to visualize the parsed database and look for patterns is also given. less

关键词： MACCS Key Python programming language Homo sapiens Malus domestica Chemical data structure Protein data structure Molecular data processing tools Database retrieval Compound–protein visualization

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