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      ImageGP: An easy‐to‐use data visualization web server for scientific researchers

      1 , 2 , 3 , 4 , 1
      iMeta
      Wiley

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          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2213140e283">Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user‐friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics‐data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP ( <a data-untrusted="" href="http://www.ehbio.com/ImageGP/" id="d2213140e285" target="xrefwindow">http://www.ehbio.com/ImageGP/</a>) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet‐lab and dry‐lab. </p><p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d2213140e290">Representative visualization results of ImageGP. ImageGP supports 16 types of images (including heatmap, volcano plot, enrichment bubble plot) and four types of online analysis with up to 26 parameters for customization. <div class="boxed-text panel" id="d2213140e292"> <a class="named-anchor" id="d2213140e292"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/3d34afb4-f07f-44d3-b7d5-b0718a70f064/PubMedCentral/image/IMT2-1-e5-g002.jpg"/> </div> <div class="panel-content"/> </div> </p><p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2213140e298"> <div class="list"> <a class="named-anchor" id="d2213140e300"> <!-- named anchor --> </a> <ul> <li id="d2213140e301"> <div class="so-custom-list-content so-ol"> <p class="first" dir="auto" id="d2213140e302">Publication‐quality visualization results.</p> </div> </li> <li id="d2213140e304"> <div class="so-custom-list-content so-ol"> <p class="first" dir="auto" id="d2213140e305">Easy to use and customize.</p> </div> </li> <li id="d2213140e307"> <div class="so-custom-list-content so-ol"> <p class="first" dir="auto" id="d2213140e308">Reproducible results with scripts.</p> </div> </li> </ul> </div> </p>

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          Most cited references15

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          Metagenomic biomarker discovery and explanation

          This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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              Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

              Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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                Author and article information

                Contributors
                Journal
                iMeta
                iMeta
                Wiley
                2770-596X
                2770-596X
                March 2022
                February 21 2022
                March 2022
                : 1
                : 1
                Affiliations
                [1 ]State Key Laboratory Breeding Base of Dao‐di Herbs National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing China
                [2 ]State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design Chinese Academy of Sciences Beijing China
                [3 ]CAS Center for Excellence in Biotic Interactions University of Chinese Academy of Sciences Beijing China
                [4 ]CAS‐JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing China
                Article
                10.1002/imt2.5
                d1b573b0-b70c-40f2-a67d-6ca7886aba4c
                © 2022

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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