ShinyLib

R Shiny based interface for browsing fitness data from transposon or CRISPRi libraries Available on Shinyapps.io!

Related publications

• Jahn et al., The energy metabolism of Cupriavidus necator in different trophic conditions, Applied and Environmental Microbiology, 2024. Link
• Miao & Jahn et al., CRISPR interference screens reveal growth–robustness tradeoffs in Synechocystis sp. PCC 6803 across growth conditions, The Plant Cell, 2023. Link
• Jahn et al., Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator, eLife, 2021. Link
• Yao et al., Pooled CRISPRi screening of the cyanobacterium Synechocystis sp PCC 6803 for enhanced industrial phenotypes, Nature Communications, 2020. Link

Getting started

Simply open the app at https://m-jahn.shinyapps.io/ShinyLib/!

If you want to run ShinyLib locally, you need to have R (optionally also Rstudio) and some of its libraries installed:

• shiny
• shinythemes
• shinyTree
• lattice
• latticeExtra
• latticetools on github
• directlabels
• tidyr
• dplyr
• dendextend
• configr

Open global.R, server.R or ui.R in RStudio and push the Run App button in Rstudio, done! You can also run the app from R console, just call runApp("path-to-ShinyLib").

Input data

• ShinyLib uses fold change and fitness data derived from next generation sequencing
• can be easily customized for use with other library-type data
• can be deployed on a shiny server for web-access
• current data sets:

name	year	organism	screening	size	conditions
Cupriavidus_BarSeq_2023	2023	Cupriavidus	BarSeq transposon	60,000	lithoautotrophy and nitrate respiration
Cupriavidus_BarSeq_2021	2021	Cupriavidus	BarSeq transposon	60,000	various carbon sources
CRISPRi_library_2022	2022	Synechocystis	CRISPRi	22,000	11 light and CO2 limitations
CRISPRi_library_2019	2019	Synechocystis	CRISPRi	10,000	low light, high light, day-night

Browsing transposon or CRISPRi mutant libraries

ShinyLib is an R Shiny based app for exploration of gene-centered data from enrichment or depletion studies. Such a library with thousands of mutants can be grown in competition experiments, leading to the depletion of growth-inhibited mutants and enrichment of faster growing mutants. By extracting the DNA and sequencing the barcode/sgRNA of the mutant population, we can investigate which genes are essential or contribute to fitness for the selected conditions.

Features:

• Displays dot plots of fold depletion/enrichment over time
• Heatmaps and clustering of proteins/genes by fitness similarity
• Fitness scores can be plotted as variable of one or two conditions
• The original data table can be filtered by pathways or single genes, and selected data can be downloaded
• Different variables can be plotted on X and Y axis, or used as conditioning variable (panel-view)
• All charts are interactive R Shiny modules and can be adjusted by many parameters

Structure

ShinyLib consists of a set of R scripts that determine the functionality.

• global.R loads the *.Rdata data sets and the accompanying *.yml configuration files.
• server.R contains the main body of functions. The server obtains input parameters from the GUI and adjusts the graphical output accordingly (changes charts on the fly)
• ui.R The GUI contains the interactive modules such as sliders and check boxes.
• dotplot.R, heatmap.R,fitness.R Plotting functions for each tab
• custom_themes.R contains a set of customized lattice themes
• custom_panel_functions.R contains a set of custom lattice panel functions
• helpbox.R contains info boxes for help, contact, and background information