Differential Expression Analysis of Nothobranchius furzeri Transposable Elements from RNA-seq Data
- 1Leonard Davis School of Gerontology, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA
- 2Master's program in Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA
- 3Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, California 90089, USA
- 4Biochemistry and Molecular Medicine Department, USC Keck School of Medicine, Los Angeles, California 90089, USA
- 5USC Norris Comprehensive Cancer Center, Epigenetics and Gene Regulation, Los Angeles, California 90089, USA
- 6USC Stem Cell Initiative, Los Angeles, California 90089, USA
- ↵7Correspondence: berenice.benayoun{at}usc.edu
Abstract
Transposable elements (TEs) comprise large fractions of eukaryotic genomes, but their repetitive nature and high copy number makes bioinformatic analyses more complex. Here, we report three robust pipelines to analyze TE expression from RNA-seq data in a non-model organism, the African turquoise killifish Nothobranchius furzeri. Our protocol can be run with either a genomic or transcriptomic reference depending on available computational resources, with options both for limited memory usage and for more computationally intensive analyses. Our protocol leverages both standard software for classical RNA-seq analysis pipelines as well as software specialized for TEs. This protocol uses input RNA-seq data from Illumina reads and can use data in either single-end or paired-end layout. Here, we show how to start from input RNA-seq data from aging killifish tissues using a publicly available data set from which we take single and paired reads, trim adapters, align and count trimmed reads, and perform differential expression analyses for TEs.
Footnotes
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From the African Turquoise Killifish collection, edited by Anne Brunet.
