Abstract
Data from high-throughput RNA sequencing require the development of more sophisticate bioinformatics tools to perform optimal gene expression analysis. Several R libraries are well considered for differential expression analyses but according to recent comparative studies, there is still an overall disagreement about which one is the most appropriate for each experiment. The applicable R libraries mainly depend on the presence or not of a reference genome and the number of replicates per condition. Here it is presented DEgenes Hunter, a RNA-seq analysis workflow for the detection of differentially expressed genes (DEGs) in organisms without genomic reference. The first advantage of DEgenes Hunter over other available solutions is that it is able to decide the most suitable algorithms to be employed according to the number of biological replicates provided in the sample. The different workflow branches allow its automatic self-customisation depending on the input data, when used by users without advanced statistical and programming skills. All applicable libraries served to obtain their respective DEGs and, as another advantage, genes marked as DEGs by all R packages employed are considered ‘common DEGs’, showing the lowest false discovery rate compared to the ‘complete DEGs’ group. A third advantage of DEgenes Hunter is that it comes with an integrated quality control module to discard or disregard low quality data before and after preprocessing. The ‘common DEGs’ are finally submitted to a functional gene set enrichment analysis (GSEA) and clustering. All results are provided as a PDF report.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gao, D., Kim, J., Kim, H., Phang, T.L., Selby, H., Tan, A.C., Tong, T.: A survey of statistical software for analysing rna-seq data. Human Genomics 5(1), 56 (2010)
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C.E., Socci, N.D., Betel, D.: Comprehensive evaluation of differential gene expression analysis methods for rna-seq data. Genome Biol. 14(9), R95 (2013)
Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., et al.: A comprehensive evaluation of normalization methods for illumina high-throughput rna sequencing data analysis. Briefings in Bioinformatics 14(6), 671–683 (2013)
Benzekri, H., Armesto, P., Cousin, X., Rovira, M., Crespo, D., Merlo, M.A., Mazurais, D., Bautista, R., Guerrero-Fernández, D., Fernandez-Pozo, N., et al.: De novo assembly, characterization and functional annotation of senegalese sole (solea senegalensis) and common sole (solea solea) transcriptomes: integration in a database and design of a microarray. BMC Genomics 15(1), 952 (2014)
Robinson, M.D., Oshlack, A., et al.: A scaling normalization method for differential expression analysis of rna-seq data. Genome Biol. 11(3), R25 (2010)
Love, M., Anders, S., Huber, W.: Differential analysis of rna-seq data at the gene level using the deseq2 package (2013)
Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edger: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010)
Law, C.W., Chen, Y., Shi, W., Smyth, G.K.: Voom: Precision weights unlock linear model analysis tools for rna-seq read counts. Preprint 2013 (2013)
Tarazona, S., García, F., Ferrer, A., Dopazo, J., Conesa, A.: Noiseq: A rna-seq differential expression method robust for sequencing depth biases. EMBnet Journal 17(B), 18–19 (2012)
Chen, H., Boutros, P.C.: Venndiagram: A package for the generation of highly-customizable venn and euler diagrams in r. BMC Bioinformatics 12(1), 35 (2011)
Wickham, H.: ggplot2: elegant graphics for data analysis. Springer (2009)
Soneson, C.: Compcoder-an r package for benchmarking differential expression methods for rna-seq data. Bioinformatics, btu324 (2014)
Alexa, A., Rahnenfuhrer, J.: topGO: enrichment analysis for gene ontology. R package version 2.8 (2010)
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.: cluster: Cluster Analysis Basics and Extensions. R package version 1.15.3 — For new features, see the ‘Changelog’ file (in the package source) (2014)
Soneson, C., Delorenzi, M.: A comparison of methods for differential expression analysis of rna-seq data. BMC Bioinformatics 14(1), 91 (2013)
Kvam, V.M., Liu, P., Si, Y.: A comparison of statistical methods for detecting differentially expressed genes from rna-seq data. American Journal of Botany 99(2), 248–256 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
González Gayte, I., Bautista Moreno, R., Claros, M.G. (2015). DEgenes Hunter - A Self-customised Gene Expression Analysis Workflow for Non-model Organisms. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9044. Springer, Cham. https://doi.org/10.1007/978-3-319-16480-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-16480-9_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16479-3
Online ISBN: 978-3-319-16480-9
eBook Packages: Computer ScienceComputer Science (R0)