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ui.R
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ui.R
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library(shiny)
library(shinydashboard)
library(DT)
dashboardPage(
skin = "black",
dashboardHeader(
title = "PhenoMatcher"
),
dashboardSidebar(
sidebarMenu(
menuItem("Query", tabName = "tool",
icon = icon("search", lib="font-awesome")
),
menuItem("About", tabName = "about",
icon = icon("info-circle", lib="font-awesome")
)
)
),
dashboardBody(
tabItems(
tabItem(tabName = "tool",
fluidRow(
column(width = 6,
box(width = NULL,
p("Search by HPO terms of your patient",style="color:dimgray;font-size: 15px"),
textAreaInput('ptHPO',NULL,value = "HP:0003302;HP:0002650;HP:0009830;HP:0000978",placeholder="Enter a list of HPO terms seperated by semicolons"),
p("Patient ID", style="color:dimgray;font-size: 15px"),
textInput('ptID',NULL,value = "sample1",placeholder="Enter the patient ID for sample identification"),
actionButton('go', 'Go'),
p("(This will take a few minutes)",style="color:dimgray;font-size: 13px")
)
),
column(width = 6,br(),
textOutput('hposum'),
textOutput('validhpocount'),
textOutput('unvalidahpo'))
),
fluidRow(
column(width = 12,
box(width = NULL,
p("Output: gene prioritization by phenotypic similarity",style="color:dimgray;font-size: 15px"),
downloadButton("downloadData", "Download"),
br(""),
solidHeader = TRUE,
collapsible = TRUE,
div(style = 'overflow-x: scroll', DT::dataTableOutput('table1'))
)
)
)
),
tabItem(tabName = "about",
p("What is PhenoMatcher", style="color:darkcyan"),
HTML("<p>PhenoMatcher is developed as part of the exome reanalysis pipeline for the paper <i>Post-reporting reanalysis of
exome sequencing data – molecular diagnostic and clinical genomic outcomes</i>, by Liu <i>et al</i>. The code used in this
program is a re-implementation and extension of the algorithm from a previous publication by James <i>et al</i>, <i>A visual
and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.</i> (2016)
Genome Med. 8:13. The algorithm was used in the publication by Posey and Harel <i>et al</i>, <i>Resolution of disease
phenotypes sesulting from multilocus genomic variation</i> (2017) N Engl J Med 376: 21-31.
<br>
<br>PhenoMatcher takes human phenotype ontology (HPO) term sets as inputs, and calculates semantic similarity scores
between the input term sets and all disease genes reported in "),
a(href="https://www.omim.org/", "OMIM."),
p("How to interpret the results",style="color:darkcyan"),
HTML("<p>PhenoMatcher annotates each disease-associated gene with:
<br>1. entrez_gene_symbol: gene symbols of Entrez genes
<br>2. disease_id_max: the OMIM disease identifier corresponding to the disease gene. When the same gene is associated with
multiple diseases, the best matching disease ID is shown.
<br>3. PhenoMatcher_score_max: semantic similarity score between the input HPO term set and the disease term set. When
the same gene is associated with multiple diseases, the highest matching score is shown.
<br>4. dz_ID_all: similar to # 2, with all disease identifiers listed
<br>5. scores: similar to # 3, with all matching scores listed
<br>6. ID: patient identification
<br>7. Patient_HPO: input HPO terms"),
p("Additional documentation",style="color:darkcyan"),
HTML("<p>The source codes and documentation can be found in the GitHub link: "),
a(href="https://github.com/liu-lab/exome_reanalysis", "https://github.com/liu-lab/exome_reanalysis."),
p("Contact us", style="color:darkcyan"),
)
)
)
)