GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form Data-Driven Hypotheses

Abstract

The NCBI Gene Expression Omnibus (GEO) provides tools to query and download transcriptomic data. However, less than 4% of microbial experiments include the sample group annotations required to assess differential gene expression for high-throughput reanalysis, and data deposited after 2014 universally lack these annotations. Our algorithm GAUGE (general annotation using text/data group ensembles) automatically annotates GEO microbial data sets, including microarray and RNA sequencing studies, increasing the percentage of data sets amenable to analysis from 4% to 33%. Eighty-nine percent of GAUGE-annotated studies matched group assignments generated by human curators. To demonstrate how GAUGE annotation can lead to scientific insight, we created GAPE (GAUGE-annotated Pseudomonas aeruginosa and Escherichia coli transcriptomic compendia for reanalysis), a Shiny Web interface to analyze 73 GAUGE-annotated P. aeruginosa studies, three times more than previously available. GAPE analysis revealed that PA3923, a gene of unknown function, was frequently differentially expressed in more than 50% of studies and significantly coregulated with genes involved in biofilm formation. Follow-up wet-bench experiments demonstrate that PA3923 mutants are indeed defective in biofilm formation, consistent with predictions facilitated by GAUGE and GAPE. We anticipate that GAUGE and GAPE, which we have made freely available, will make publicly available microbial transcriptomic data easier to reuse and lead to new data-driven hypotheses. IMPORTANCE GEO archives transcriptomic data from over 5,800 microbial experiments and allows researchers to answer questions not directly addressed in published papers. However, less than 4% of the microbial data sets include the sample group annotations required for high-throughput reanalysis. This limitation blocks a considerable amount of microbial transcriptomic data from being reused easily. Here, we demonstrate that the GAUGE algorithm could make 33% of microbial data accessible to parallel mining and reanalysis. GAUGE annotations increase statistical power and, thereby, make consistent patterns of differential gene expression easier to identify. In addition, we developed GAPE (GAUGE-annotated Pseudomonas aeruginosa and Escherichia coli transcriptomic compendia for reanalysis), a Shiny Web interface that performs parallel analyses on P. aeruginosa and E. coli compendia. Source code for GAUGE and GAPE is freely available and can be repurposed to create compendia for other bacterial species.

Generative neural networks separate common and specific transcriptional responses

“…Compared to prior work using manually curated datasets, which required laborious manual grouping 6,7,17 , SOPHIE demonstrates consistent results but using an automated process. In short, SOPHIE identifies the same common patterns but in a fast and scalable way.…”

“…Finally, when we extended this analysis to a different organism, P. aeruginosa , we observed the same concordance (R 2 = 0.449) between SOPHIE-generated percentiles compared to those generated using a manually validated dataset, GAPE (Figure 2D). 16 GAPE contained a collection of 73 array experiments from the GPL84 platform. We found a significant over-representation (p=1e-139) of SOPHIE identified common DEGs within the GAPE set of common DEGs.…”

“…We performed this same correlation analysis comparing SOPHIE trained on the P. aeruginosa compendium with percentiles generated from the GAPE project from the Stanton lab (https://github.com/DartmouthStantonLab/GAPE). 16 The GAPE dataset contained ANOVA statistics generated for 73 P. aeruginosa microarray experiments using the Affymetrix platform GPL84. We downloaded the differential expression statistics for 73 array experiments from the associated repository (https://github.com/DartmouthStantonLab/GAPE/blob/main/Pa_GPL84_refine_ANOVA_List _unzip.rds).…”

CF-Seq, An Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies

“…However, none are specifically geared towards CF pathogen research, and there is room to expand on their functionality [Table 2]. Our own lab has previously published tools to make publicly available data more accessible to CF researchers 29,30 , but these tools focus on the most commonly studied CF pathogensnamely Pseudomonas aeruginosa and Staphylococcus aureus -and don't include data sets on many of the other clinically relevant species listed in Table 1. Building on our prior work, we present the R Shiny web application CF-Seq.…”

Computationally efficient assembly of a Pseudomonas aeruginosa gene expression compendium

“…The P. aeruginosa community has long supported the development and widespread use of databases, hubs and analysis tools, such as The Pseudomonas Genome Database (10), BACTOME (11), the International Pseudomonas Consortium Database (12), the Pseudomonas aeruginosa metabolome database (13), the Pseudomonas aeruginosa transcriptome viewer (14), and the shiny app with algorithmically annotated datasets, GAPE (15). Tools have also been developed that utilize public data from across many experiments in concert, such as the ADAGE web server which enables the exploration of P. aeruginosa microarray data after processing by a machine learning algorithm (16).…”