|ZFIN ID: ZDB-PUB-141217-18|
Analysis of RNAseq datasets from a comparative infectious disease zebrafish model using GeneTiles bioinformatics
Veneman, W.J., de Sonneville, J., van der Kolk, K.J., Ordas, A., Al-Ars, Z., Meijer, A.H., Spaink, H.P.
|Source:||Immunogenetics 67(3): 135-47 (Journal)|
|Registered Authors:||de Sonneville, Jan, Meijer, Annemarie H., Spaink, Herman P.|
|Microarrays:||GEO:GSE42846, GEO:GSE44351, GEO:GSE57792|
|PubMed:||25503064 Full text @ Immunogenetics|
Veneman, W.J., de Sonneville, J., van der Kolk, K.J., Ordas, A., Al-Ars, Z., Meijer, A.H., Spaink, H.P. (2015) Analysis of RNAseq datasets from a comparative infectious disease zebrafish model using GeneTiles bioinformatics. Immunogenetics. 67(3):135-47.
ABSTRACTWe present a RNA deep sequencing (RNAseq) analysis of a comparison of the transcriptome responses to infection of zebrafish larvae with Staphylococcus epidermidis and Mycobacterium marinum bacteria. We show how our developed GeneTiles software can improve RNAseq analysis approaches by more confidently identifying a large set of markers upon infection with these bacteria. For analysis of RNAseq data currently, software programs such as Bowtie2 and Samtools are indispensable. However, these programs that are designed for a LINUX environment require some dedicated programming skills and have no options for visualisation of the resulting mapped sequence reads. Especially with large data sets, this makes the analysis time consuming and difficult for non-expert users. We have applied the GeneTiles software to the analysis of previously published and newly obtained RNAseq datasets of our zebrafish infection model, and we have shown the applicability of this approach also to published RNAseq datasets of other organisms by comparing our data with a published mammalian infection study. In addition, we have implemented the DEXSeq module in the GeneTiles software to identify genes, such as glucagon A, that are differentially spliced under infection conditions. In the analysis of our RNAseq data, this has led to the possibility to improve the size of data sets that could be efficiently compared without using problem-dedicated programs, leading to a quick identification of marker sets. Therefore, this approach will also be highly useful for transcriptome analyses of other organisms for which well-characterised genomes are available.