Abstract
RNA-based high-throughput sequencing technologies provide a huge amount of reads from transcripts. In addition to expression analyses, transcriptome reconstruction, or isoform detection, they could be useful for detection of gene variations, in particular micro-variations (single nucleotide polymorphisms [SNPs] and indels). Gene variations are usually based on homogenous (one single individual) DNA-seq data, but this study aims the usage of heterogeneous (several individuals) RNA-seq data to obtain clues about gene variability of a population. Therefore, new algorithms or workflows are required to fill this gap, usually disregarded. Here it is presented an automated workflow based on existing software to predict micro-variations from RNA-seq data using a genome or a transcriptome as reference. It can deal with organism whose genome sequence is known and well-annotated, as well as non-model organism where only draft genomes or transcriptomes are available. Mapping is based on STAR in both cases. Micro-variation detection relies on GATK (combining Mutect2 and HaplotypeCaller) and VarScan since they are able to provide reliable results from RNA-seq reads. The workflow has been tested with reads from normal and diseased lung from patients having small-cell lung carcinoma. Human genome, as well as human transcriptome, were used as reference and then compared: from the initial 120 000 micro-variations, only 267 were predicted by at least two algorithm in the exome of patients. The workflow was tested in non-model organisms such as Senegalese sole, using its transcriptome as reference, to determine micro-variations in sole larvae exposed to different salinities. Therefore, the workflow seems to produce robust and reliable micro-variations in coding genes based on RNA-seq, irrespective of the nature of the reference sequence. We think that this paves the way to correlate micro-variations and differentially expressed genes in non-model organisms with the aim of foster breeding plans.
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Acknowledgments
This work was funded by the NeumoSur grants 12/2015 and 14/2016 as well as projects RTA2017-00054-C03-03, AGL2017-83370-C3-3-R and TIN2017-88728-C2-1-R co-funded by MCIU/AEI/FEDER (Spanish Ministerio de Ciencia, Innovación y Universidad, Spanish Agencia Estatal de Investigación, and European Regional Development Fund 2014–2020). The authors also thankfully acknowledge the computer resources and the technical support provided by the Andalusian Platform for Bioinformatics of the University of Malaga.
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Espinosa, E., Arroyo, M., Larrosa, R., Manchado, M., Claros, M.G., Bautista, R. (2020). Micro-Variations from RNA-seq Experiments for Non-model Organisms. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_48
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