Galaxy Server
Galaxy Server - Related Videos
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Cited by 8 •
2017
Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
05:08
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Cited by 1 •
2025
Protein design involves the construction of amino acid sequences and the incorporation of specific motifs to create functional variants. This approach is critical for the development of antimicrobial peptides (AMPs) to combat antibiotic-resistant pathogens. This paper presents a procedure for protein construction using various bioinformatics tools.
09:52
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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2025
Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.
14:26
Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotic Cells
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Cited by 59 •
2016
This paper presents a sensitive method called Circle-Seq for purifying extrachromosomal circular DNA (eccDNA). The method encompasses column purification, removal of remaining linear chromosomal DNA, rolling-circle amplification and high-throughput sequencing. Circle-Seq is applicable to genome-scale screening of eukaryotic eccDNA and studying genome instability and copy-number variation.
08:42
Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems
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Cited by 15 •
2015
Fagot & Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.
FAQs
Galaxy records the datasets, tools, parameter settings, outputs, and sequence of actions in analysis histories. This record allows a researcher to inspect how a statistical result was produced rather than relying only on a final table or figure. Histories can also be shared, giving collaborators or students a detailed account of the computational steps behind an analysis.
Each analysis tool provides a way to apply a selected computational or statistical operation to uploaded data. Researchers choose the relevant parameters before submitting a job, and Galaxy sends that job to configured computing resources. The combination of tool choice, parameter values, input data, and resulting output defines the computational details needed to interpret the analysis.
An analysis history documents the steps taken for a particular dataset, whereas a reusable workflow organizes those steps into a repeatable procedure. Sharing workflows helps researchers apply the same sequence of statistical operations to other datasets and supports collaboration. This separation between a specific run and a reusable process can make recurring analyses easier to communicate and reproduce.
Galaxy can support several stages of a quantitative workflow, including data preparation, visualization, hypothesis testing, and regression. These tasks can be arranged as related steps rather than treated as isolated calculations. Connecting preparation with subsequent statistical analysis helps users preserve the progression from an input dataset to quantitative results and visual summaries.
A researcher begins by uploading the dataset, selects an appropriate analysis tool, specifies its parameters, and submits the job to available computing resources. The resulting files and settings are retained in the analysis history. Researchers can then organize related steps into a workflow or share the recorded history, preserving the path from data input to statistical output.
Its web-based interface lets users apply statistical methods without programming, which can help students focus on data preparation, interpretation, and analytical reasoning. In research settings, shared histories and workflows give collaborators a common record of inputs, settings, and outputs. These features support transparent discussion of quantitative methods and make complex statistical analyses easier to communicate.