Using Genome-wide Expression Compendia to Study Microorganisms

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2022 Aug 26

PMID 36016717

Authors

Alexandra J Lee

Taylor Reiter

Georgia Doing

Julia Oh

Deborah A Hogan

Casey S Greene

Affiliations

Soon will be listed here.

Abstract

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

References

Ramage G, Saville S, Wickes B, Lopez-Ribot J . Inhibition of Candida albicans biofilm formation by farnesol, a quorum-sensing molecule. Appl Environ Microbiol. 2002; 68(11):5459-63. PMC: 129887. DOI: 10.1128/AEM.68.11.5459-5463.2002. View

Yang Y, Dudoit S, Luu P, Lin D, Peng V, Ngai J . Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002; 30(4):e15. PMC: 100354. DOI: 10.1093/nar/30.4.e15. View

Tan J, Huyck M, Hu D, Zelaya R, Hogan D, Greene C . ADAGE signature analysis: differential expression analysis with data-defined gene sets. BMC Bioinformatics. 2017; 18(1):512. PMC: 5700673. DOI: 10.1186/s12859-017-1905-4. View

Arrieta-Ortiz M, Hafemeister C, Bate A, Chu T, Greenfield A, Shuster B . An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network. Mol Syst Biol. 2015; 11(11):839. PMC: 4670728. DOI: 10.15252/msb.20156236. View

Chaves-Moreno D, Wos-Oxley M, Jauregui R, Medina E, Oxley A, Pieper D . Exploring the transcriptome of Staphylococcus aureus in its natural niche. Sci Rep. 2016; 6:33174. PMC: 5027550. DOI: 10.1038/srep33174. View

Ibraim I, Dornelles Parise M, Parise D, Sfeir M, Castro T, Wattam A . Transcriptome profile of Corynebacterium pseudotuberculosis in response to iron limitation. BMC Genomics. 2019; 20(1):663. PMC: 6701010. DOI: 10.1186/s12864-019-6018-1. View

Goncalves R, Musen M . The variable quality of metadata about biological samples used in biomedical experiments. Sci Data. 2019; 6:190021. PMC: 6380228. DOI: 10.1038/sdata.2019.21. View

Huttenhower C, Hibbs M, Myers C, Troyanskaya O . A scalable method for integration and functional analysis of multiple microarray datasets. Bioinformatics. 2006; 22(23):2890-7. DOI: 10.1093/bioinformatics/btl492. View

Chaves-Moreno D, Wos-Oxley M, Jauregui R, Medina E, Oxley A, Pieper D . Application of a Novel "Pan-Genome"-Based Strategy for Assigning RNAseq Transcript Reads to Staphylococcus aureus Strains. PLoS One. 2015; 10(12):e0145861. PMC: 4696825. DOI: 10.1371/journal.pone.0145861. View

10.

Palma M, DeLuca D, Worgall S, Quadri L . Transcriptome analysis of the response of Pseudomonas aeruginosa to hydrogen peroxide. J Bacteriol. 2003; 186(1):248-52. PMC: 303446. DOI: 10.1128/JB.186.1.248-252.2004. View

11.

Handwerger S, Tomasz A . Alterations in kinetic properties of penicillin-binding proteins of penicillin-resistant Streptococcus pneumoniae. Antimicrob Agents Chemother. 1986; 30(1):57-63. PMC: 176435. DOI: 10.1128/AAC.30.1.57. View

12.

Yanagihara K, Tomono K, Kaneko Y, Miyazaki Y, Tsukamoto K, Hirakata Y . Role of elastase in a mouse model of chronic respiratory Pseudomonas aeruginosa infection that mimics diffuse panbronchiolitis. J Med Microbiol. 2003; 52(Pt 6):531-535. DOI: 10.1099/jmm.0.05154-0. View

13.

Kodama Y, Shumway M, Leinonen R . The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 2011; 40(Database issue):D54-6. PMC: 3245110. DOI: 10.1093/nar/gkr854. View

14.

Ghoul M, Mitri S . The Ecology and Evolution of Microbial Competition. Trends Microbiol. 2016; 24(10):833-845. DOI: 10.1016/j.tim.2016.06.011. View

15.

Price A, Gibas C . The quantitative impact of read mapping to non-native reference genomes in comparative RNA-Seq studies. PLoS One. 2017; 12(7):e0180904. PMC: 5507458. DOI: 10.1371/journal.pone.0180904. View

16.

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

17.

Way G, Zietz M, Rubinetti V, Himmelstein D, Greene C . Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations. Genome Biol. 2020; 21(1):109. PMC: 7212571. DOI: 10.1186/s13059-020-02021-3. View

18.

Oshlack A, Wakefield M . Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009; 4:14. PMC: 2678084. DOI: 10.1186/1745-6150-4-14. View

19.

Doing G, Koeppen K, Occipinti P, Harty C, Hogan D . Conditional antagonism in co-cultures of Pseudomonas aeruginosa and Candida albicans: An intersection of ethanol and phosphate signaling distilled from dual-seq transcriptomics. PLoS Genet. 2020; 16(8):e1008783. PMC: 7480860. DOI: 10.1371/journal.pgen.1008783. View

20.

Larimer F, Chain P, Hauser L, Lamerdin J, Malfatti S, Do L . Complete genome sequence of the metabolically versatile photosynthetic bacterium Rhodopseudomonas palustris. Nat Biotechnol. 2004; 22(1):55-61. DOI: 10.1038/nbt923. View