FaDA: A web application for regular laboratory data analyses.
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ABSTRACT:
SUBMITTER: Danger R
PROVIDER: S-EPMC8687579 | biostudies-literature |
REPOSITORIES: biostudies-literature
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2024-11-21 | GSE271422 | GEO
Project description:BackgroundNormal mode analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. NMA is especially useful for large biomolecular assemblies, such as transmembrane channels or virus capsids. NMA relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant.ResultsWe developed a web-based server to perform normal modes calculations and different types of analyses. Starting from a structure file provided by the user in the PDB format, the server calculates the normal modes and subsequently offers the user a series of automated calculations; normalized squared atomic displacements, vector field representation and animation of the first six vibrational modes. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to analyze the results with their favorite software, raw results can also be downloaded. The application is available on http://www.bioinfo.no/tools/normalmodes. We present here the underlying theory, the application architecture and an illustration of its features using a large transmembrane protein as an example.ConclusionWe built an efficient and modular web application for normal mode analysis of proteins. Non specialists can easily and rapidly evaluate the degree of flexibility of multi-domain protein assemblies and characterize the large amplitude movements of their domains.
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Project description:In plants, differentiated somatic cells exhibit an exceptional ability to regenerate new tissues, organs, or whole plants. Recent studies have unveiled core genetic components and pathways underlying cellular reprogramming and de novo tissue regeneration in plants. Although high-throughput analyses have led to key discoveries in plant regeneration, a comprehensive organization of large-scale data is needed to further enhance our understanding of plant regeneration. Here, we collected all currently available transcriptome datasets related to wounding responses, callus formation, de novo organogenesis, somatic embryogenesis, and protoplast regeneration to construct REGENOMICS, a web-based application for plant REGENeration-associated transcriptOMICS analyses. REGENOMICS supports single- and multi-query analyses of plant regeneration-related gene-expression dynamics, co-expression networks, gene-regulatory networks, and single-cell expression profiles. Furthermore, it enables user-friendly transcriptome-level analysis of REGENOMICS-deposited and user-submitted RNA-seq datasets. Overall, we demonstrate that REGENOMICS can serve as a key hub of plant regeneration transcriptome analysis and greatly enhance our understanding on gene-expression networks, new molecular interactions, and the crosstalk between genetic pathways underlying each mode of plant regeneration. The REGENOMICS web-based application is available at http://plantregeneration.snu.ac.kr.
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Project description:Abstract:The volume of transcriptome data is growing exponentially due to rapid improvement of experimental technologies. In response, large central resources such as those of the National Center for Biotechnology Information (NCBI) are continually adapting their computational infrastructure to accommodate this large influx of data. New and specialized databases, such as Transcriptome Shotgun Assembly Sequence Database (TSA) and Sequence Read Archive (SRA), have been created to aid the development and expansion of centralized repositories. Although the central resource databases are under continual development, they do not include automatic pipelines to increase annotation of newly deposited data. Therefore, third-party applications are required to achieve that aim. Here, we present an automatic workflow and web application for the annotation of transcriptome data. The workflow creates secondary data such as sequencing reads and BLAST alignments, which are available through the web application. They are based on freely available bioinformatics tools and scripts developed in-house. The interactive web application provides a search engine and several browser utilities. Graphical views of transcript alignments are available through SeqViewer, an embedded tool developed by NCBI for viewing biological sequence data. The web application is tightly integrated with other NCBI web applications and tools to extend the functionality of data processing and interconnectivity. We present a case study for the species Physalis peruviana with data generated from BioProject ID 67621. Database:URL: http://www.ncbi.nlm.nih.gov/projects/physalis/.
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Project description:Circularized Chromosome Conformation Capture followed by deep sequencing (4C-Seq) is a powerful technique to identify genome-wide partners interacting with a pre-specified genomic locus. Here, we present a computational and statistical approach to analyze 4C-Seq data generated from both enzyme digestion and sonication fragmentation-based methods. We implemented a command line software tool and a web interface called w4CSeq, which takes in the raw 4C sequencing data (FASTQ files) as input, performs automated statistical analysis and presents results in a user-friendly manner. Besides providing users with the list of candidate interacting sites/regions, w4CSeq generates figures showing genome-wide distribution of interacting regions, and sketches the enrichment of key features such as TSSs, TTSs, CpG sites and DNA replication timing around 4C sites.Availability and implementationUsers can establish their own web server by downloading source codes at https://github.com/WGLab/w4CSeq Additionally, a demo web server is available at http://w4cseq.wglab.org CONTACT: kaiwang@usc.edu or wangelu@usc.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Project description:Individuals vary greatly in their willingness to select and persist in effortful tasks, even when high-effort will knowingly result in high-reward. Individuals who select and successively complete effortful, goal-directed tasks can be described as industrious. Trying to increase one's industriousness is desirable from a productivity standpoint, yet intrinsically challenging given that effort expenditure is generally aversive. Here we show that in laboratory rats, a basic physical exercise regimen (20 min/day, five days/week) is sufficient to increase industriousness across a battery of subsequent testing tasks. Exercised rats outperformed their non-exercised counterparts in tasks designed to tax effort expenditure, strategic decision-making, problem solving and persistence. These increases in performance led to quicker reward obtainment and greater reward gain over time, and could not be accounted for simply by increased locomotor activity. Our results suggest that a basic exercise regimen can enhance effortful goal-directed behaviour in goal-directed tasks, which highlights a potential productivity benefit of staying physically active.
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Project description:BackgroundSystems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting.ResultsWe present SBMLmod, a Python-based web application and service, that automates integration of high throughput data into SBML models. Subsequent steady state analysis is readily accessible via the web service COPASIWS. We illustrate the utility of SBMLmod by integrating gene expression data from different healthy tissues as well as from a cancer dataset into a previously published model of mammalian tryptophan metabolism.ConclusionSBMLmod is a user-friendly platform for model modification and simulation. The web application is available at http://sbmlmod.uit.no , whereas the WSDL definition file for the web service is accessible via http://sbmlmod.uit.no/SBMLmod.wsdl . Furthermore, the entire package can be downloaded from https://github.com/MolecularBioinformatics/sbml-mod-ws . We envision that SBMLmod will make automated model modification and simulation available to a broader research community.
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