ArticlemSystems2021
GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form Data-Driven Hypotheses.
Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Analyzing qPCR data: Better practices to facilitate rigor and reproducibility.Biochemistry and biophysics reports · 2025Article
- E.PathDash, pathway activation analysis of publicly available pathogen gene expression data.mSystems · 2024Article
- Article
- Rocket-miR, a translational launchpad for miRNA-based antimicrobial drug development.mSystems · 2023Article
- Analysis ofbioRxiv : the preprint server for biology · 2023Article
- Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia.mSystems · 2023Article
- SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.Genomics, proteomics & bioinformatics · 2022Article
- CF-Seq, an accessible web application for rapid re-analysis of cystic fibrosis pathogen RNA sequencing studies.Scientific data · 2022Article
- Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.