hittite j sci eng

Hittite Journal of Science and Engineering

2148-4171

Hitit University

10.17350/HJSE19030000163

A Modified Soft-thresholding Approach in the Transcriptomic Analysis of Adaptation of E.coli to Alternating Substrate Conditions

Karabekmez

Muhammed Erkan

İstanbul Medeniyet University,Department of Bioengineering, İstanbul, Turkey

12 31 2019

6 4 315 318

2014

Hittite Journal of Science and Engineering

The expression of genes that are functionally related is considered to change together in response to deterioration of internal or external order. The system-level analysis of these changes has become widespread in recent years. Weighted gene co-expression network analysis WGCNA is an important tool in the literature. This method has two options in the form of hard and soft thresholding. The power function is used commonly in soft thresholding option. The other alternative of soft thresholding, symmetric sigmoid function, may give less importance to the meaningful co-expression data and not preferred frequently. Both functions has some drawbacks. In this study, it was tried to increase the efficiency of WGCNA approach by using asymmetric sigmoid function. RNA-seq dataset on adaptation of E.coli to alternating substrate conditions was re-investigated with this modified approach and its use was proven by GO and pathway enrichment analysis

WGCNA Transcriptomics Asymmetric Sigmoid Function

1. Zhang B and Horvath S. A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, (2005) Article 17 PMID: 16646834.

2. Dong J, Horvath S. Understanding Network Concepts in Modules. BMC Systems Biology, 1:24 (2007) PMID: 17547772 PMCID: PMC3238286.

3. Horvath S, Dong J. Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): (2008) e1000117 PMID: 18704157 PMCID: PMC2446438.

4. Yip A, Horvath S. Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics, 8:22. (2007) PMID: 17250769 PMCID: PMC1797055.

5. Li A, Horvath S. Network Neighborhood Analysis with the multinode topological overlap measure. Bioinformatics. (2006) PMID: 17110366 doi:10.1093/bioinformatics/btl581.

6. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut library for R. Bioinformatics. November/btm563 (2007) PMID: 18024473.

7. Aten JE, Fuller TF, Lusis AJ, Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Systems Biology 2008, 2:34 (2008) PMID: 18412962 PMCID: PMC2387136.

8. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PloS Comp Biol. 7(1): (2011) e1001057 PMID: 21283776 PMCID: PMC3024255.

9. Song L, Langfelder P, Horvath S. Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics 14:5 (2013) PMID: 23323760 DOI: 10.1186/1471- 2105-14-5.

10. Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54 (2007) PMID: 18031580.

11. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559. (2008) PMID: 19114008 PMCID: PMC2631488.

12. Xue Z, Huang K, Cai C, Cai L, Jiang CY, Feng Y, Liu Z, Zeng Q, Cheng L, Sun YE, Liu JY, Horvath S, Fan G. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature. 2013 Jul 28. (2013) doi: 10.1038/nature12364 PMID: 23892778.

13. van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, de Kovel CG, Janson E, Strengman E, Langfelder P, Kahn RS, van den Berg LH, Horvath S, Ophoff RA. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects BMC Genomics. 2012 Nov 17;13(1):636 (2012) PMID: 23157493.

14. Wang, W., Jiang, W., Hou, L., Duan, H., Wu, Y., Xu, C., ... & Zhang, D. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI. BMC genomics, 18(1), 872. (2017).

15. Miller, J. A., Guillozet-Bongaarts, A., Gibbons, L. E., Postupna, N., Renz, A., Beller, A. E., ... & Szafer, A. Neuropathological and transcriptomic characteristics of the aged brain. eLife, 6. (2017).

16. Simon, S., Sagasser, S., Saccenti, E., Brugler, M. R., Schranz, M E., Hadrys, H., ... & DeSalle, R. Comparative transcriptomics reveal developmental turning points during embryogenesis of a hemimetabolous insect, the damselfly Ischnura elegans. Scientific Reports, 7(1), (2017) 13547.

17. Albert, R., & Barabási, A. L. Statistical mechanics of complex networks. Reviews of modern physics, 74(1), (2002), 47.

18. Sandberg, T. E., Lloyd, C. J., Palsson, B. O., & Feist, A. M. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl. Environ. Microbiol., 83(13), (2017), e00410-17.

19. Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., Van Baren, M. J., ... & Pachter, L.. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology, 28(5), 511, (2010).

20. Langmead, B., & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357, (2012).

21. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11), 2498-2504, (2003).

22. Huang, D. W., Sherman, B. T., & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols, 4(1), (2008), 44.