DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells

Katherine Duchinski, Margaret Antonio, Dennis Watson, Paul Anderson

Abstract

Understanding the genetic basis of disease may lead to the development of life-saving diagnostics and therapeutics. RNA-sequencing (RNA-seq) gives a snapshot of cellular processes via high-throughput transcriptome sequencing. Meta-analysis of multiple RNA-Seq experiments has the potential to (a) elucidate gene function under different conditions and (b) compare results in replicate experiments. To simplify such meta-analyses, we created the Dataset Exploration And Curation Tool (DEACT), an interactive, user-friendly web application. DEACT allows users to (1) interactively visualize RNA-Seq data, (2) select genes of interest through the user interface, and (3) download subsets for downstream analyses. We tested DEACT using two complementary RNA-seq studies resulting from knockdown and gain-of-function FLI1 in an aggressive breast cancer cell line. We performed fixed gene-set enrichment analysis on four subsets of genes selected through DEACT. Each subset implicated different metabolic pathways, demonstrating the power of DEACT in driving downstream analysis of complementary RNA-Seq studies.

References

  1. Bioinformatics, B. (2011). Fastqc a quality control tool for high throughput sequence data. Cambridge, UK: Babraham Institute.
  2. Bolger, A. and Giorgi, F. (2014). Trimmomatic: a flexible read trimming tool for illumina ngs data. URL http://www. usadellab. org/cms/index. php.
  3. Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., Georgescu, C., and Romero, R. (2007). A systems biology approach for pathway level analysis. Genome research, 17(10):1537-1545.
  4. Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome biology, 15(12):1.
  5. Luo, W. and Brouwer, C. (2013). Pathview: an r/bioconductor package for pathway-based data integration and visualization. Bioinformatics, 29(14):1830-1831.
  6. Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D., and Woolf, P. J. (2009). Gage: generally applicable gene set enrichment for pathway analysis. BMC bioinformatics, 10(1):1.
  7. Plotly Technologies Inc. (2015). Collaborative data science.
  8. Pontes, B., Giráldez, R., and Aguilar-Ruiz, J. S. (2015). Biclustering on expression data: A review. Journal of biomedical informatics, 57:163-80.
  9. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., and Smyth, G. K. (2015). limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic acids research, page gkv007.
  10. Robinson, M. D., McCarthy, D. J., and Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1):139-140.
  11. Rogé, X. and Zhang, X. (2013). Rnaseqviewer: visualization tool for rna-seq data. Bioinformatics, page btt649.
  12. RStudio, Inc (2014). Easy web applications in R. URL: http://www.rstudio.com/shiny/.
  13. Scheiber, M. N., Watson, P. M., Rumboldt, T., Stanley, C., Wilson, R. C., Findlay, V. J., Anderson, P. E., and Watson, D. K. (2014). Fli1 expression is correlated with breast cancer cellular growth, migration, and invasion and altered gene expression. Neoplasia, 16(10):801- 813.
  14. Seo, M., Yoon, J., and Park, T. (2015). Gracomics: software for graphical comparison of multiple results with omics data. BMC genomics, 16(1):1.
  15. Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., and Pachter, L. (2012). Differential gene and transcript expression analysis of rna-seq experiments with tophat and cufflinks. Nature protocols, 7(3):562-578.
  16. Watson, D. K., Turner, D. P., Scheiber, M. N., Findlay, V. J., and Watson, P. M. (2010). Ets transcription factor expression and conversion during prostate and breast cancer progression. Open Cancer J, 3:24-39.
  17. Zhou, X., Lindsay, H., and Robinson, M. D. (2014). Robustly detecting differential expression in rna sequencing data using observation weights. Nucleic acids research, 42(11):e91-e91.
Download


Paper Citation


in Harvard Style

Duchinski K., Antonio M., Watson D. and Anderson P. (2017). DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 154-159. DOI: 10.5220/0006152901540159


in Bibtex Style

@conference{bioinformatics17,
author={Katherine Duchinski and Margaret Antonio and Dennis Watson and Paul Anderson},
title={DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={154-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006152901540159},
isbn={978-989-758-214-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells
SN - 978-989-758-214-1
AU - Duchinski K.
AU - Antonio M.
AU - Watson D.
AU - Anderson P.
PY - 2017
SP - 154
EP - 159
DO - 10.5220/0006152901540159