WoPPER: Web server for Position Related data analysis of gene Expression in Prokaryotes

Abstract

The structural and conformational organization of chromosomes is crucial for gene expression regulation in eukaryotes and prokaryotes as well. Up to date, gene expression data generated using either microarray or RNA-sequencing are available for many bacterial genomes. However, differential gene expression is usually investigated with methods considering each gene independently, thus not taking into account the physical localization of genes along a bacterial chromosome. Here, we present WoPPER, a web tool integrating gene expression and genomic annotations to identify differentially expressed chromosomal regions in bacteria. RNA-sequencing or microarray-based gene expression data are provided as input, along with gene annotations. The user can select genomic annotations from an internal database including 2780 bacterial strains, or provide custom genomic annotations. The analysis produces as output the lists of positionally related genes showing a coordinated trend of differential expression. Graphical representations, including a circular plot of the analyzed chromosome, allow intuitive browsing of the results. The analysis procedure is based on our previously published R-package PREDA. The release of this tool is timely and relevant for the scientific community, as WoPPER will fill an existing gap in prokaryotic gene expression data analysis and visualization tools. WoPPER is open to all users and can be reached at the following URL: https://WoPPER.ba.itb.cnr.it

A Coordinated Response at The Transcriptome and Interactome Level is Required to Ensure Uropathogenic Escherichia coli Survival during Bacteremia

“…Given that F1 genes were more condensed along the genome, we investigated whether we could identify clusters of genes with high average fitness values. To this end, we used Locally Adaptive Procedure (LAP) algorithms to detect groups of physically contiguous genes characterized by similar profiles [16]. We were able to detect two clusters in the aforementioned region that contain genes with a high impact on fitness.…”

“…WoPPER was used for genome enrichment, with fitness scores used as the input dataset. WoPPER [16] uses the input data to perform a local adaptive smoothing of the data over the gene coordinates. Local maxima and minima were estimated for the smoothed profile against the expected null distribution and clusters were identified.…”

Transcript Profiling of MRSA Biofilms Treated with a Halogenated Phenazine Eradicating Agent: A Platform for Defining Cellular Targets and Pathways Critical to Biofilm Survival

“…Volcano plot (Figure A) and heatmap analysis (Figure B) of our RNA‐seq results show 83 genes down‐regulated and 134 genes up‐regulated in MRSA biofilms as a result of HP‐14 treatment. We found the WoPPER analysis tool to be the most useful approach to analyzing our RNA‐seq data. The WoPPER tool enabled us to determine gene clusters up‐regulated (“activated”) and down‐regulated (“inhibited”) in response to HP‐14 (Figure C).…”

Phenazine Antibiotic‐Inspired Discovery of Bacterial Biofilm‐Eradicating Agents

“…From >2700 MRSA‐1707 biofilm gene transcripts analyzed using RNA‐seq technology, 83 transcripts were down‐regulated and 134 transcripts were up‐regulated in response to HP‐14 . With >200 gene transcripts altered from HP‐14 treatment, we utilized the WoPPER analysis tool to focus attention on gene cluster activation or inhibition. We found this approach to streamline our efforts, as 37 gene clusters were either up‐ or down‐regulated from HP‐14 treatment, including six activated gene clusters involved in iron uptake (Figure ).…”