Journal of Molecular BiologyBioinformaticsPeer-reviewed journal article
Bacteria.guru: comparative transcriptomics and co-expression database for bacterial pathogens
Peng Ken LimiD, Emilia Emmanuelle Davey, Sean Wee, Wei Song Seetoh, Jong Ching Goh, Xinghai ZhengiD, Sean Kia Ann PhangiD, Eugene Sheng Kai Seah, Janice Wan Zhen Ng, Xavier Jia Hui Wee, Aloysius Jun Hui QuekiD, Jordan JingHeng Lim, Edbert Edric Rodrigues, Hee-Soo LeeiD, Chin Yong LimiD, Wei Zhi Tan, Yuet Ruh DaniD, Bronson Lee, Samuel En Le Chee, Zachary Ze En Lim, Jia Sheng GuaniD, Ivan Jia Le TaniD, Trinidad Jeremiah Arong, Marek MutwiliD
Abstract
SummaryThe bacterial kingdom comprises unicellular prokaryotes able to establish symbioses from mutualism to parasitism. To combat bacterial pathogenicity, we need an enhanced understanding of gene function and regulation, which will mediate the development of novel antimicrobials. Gene expression can predict gene function, but there lacks a database enabling expansive inter- and intraspecific exploration of gene expression profiles and co-expression networks for bacteria. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and studied bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, co-expression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid the discovery of multi-faceted antibiotic targets. Hence, we believe bacteria.guru will facilitate future bacterial research. AvailabilityThe database and co-expression networks are freely available from https://bacteria.guru/. The sample annotations are found in the supplemental data.
- organism: bacteria (other)
- assay: bulk RNA-seq
- research focus: database / resource
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 10
- FWCI
- 0.71
- Field percentile
- top 34%
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