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Title: LSTrAP-Crowd : prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data
Authors: Hew, Benedict
Tan, Qiao Wen
Goh, William
Ng, Jonathan Wei Xiong
Mutwil, Marek
Keywords: Science::Biological sciences
Issue Date: 2020
Source: Hew, B., Tan, Q. W., Goh, W., Ng, J. W. X., & Mutwil, M. (2020). LSTrAP-Crowd : prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data. BMC Biology, 18(1), 114-. doi:10.1186/s12915-020-00846-9
Journal: BMC Biology
Abstract: Background: Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. Results: In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. Conclusions: We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.
ISSN: 1741-7007
DOI: 10.1186/s12915-020-00846-9
Schools: School of Biological Sciences 
Rights: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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