Dataset Information

Differential Expression Analysis of Nothobranchius furzeri Transposable Elements from RNA-seq Data.

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.

SUBMITTER: Teefy B

PROVIDER: S-EPMC9812909 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Differential Expression Analysis of <i>Nothobranchius furzeri</i> Transposable Elements from RNA-seq Data.

Teefy Bryan B Malone Matthew M Benayoun Bérénice A BA

Cold Spring Harbor protocols 20230103 1

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 <i>Nothobranchius furzeri</i> Our protocol can be run with either a genomic or transcriptomic reference depending on available computational resources, with options both for limited memory usag ...[more]

PMID: 36223994

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Project description:Transposable elements (TEs), also known as "jumping genes", are repetitive sequences with the capability of changing their location within the genome. They are key players in many different biological processes in health and disease. Therefore, a reliable quantification of their expression as transcriptional units is crucial to distinguish between their independent expression and the transcription of their sequences as part of canonical transcripts. TEs quantification faces difficulties of different types, the most important one being low reads mappability due to their repetitive nature preventing an unambiguous mapping of reads originating from their sequences. A large fraction of TEs fragments localizes within introns, which led to the hypothesis that intron retention (IR) can be an additional source of bias, potentially affecting accurate TEs quantification. IR occurs when introns, normally removed from the mature transcript by the splicing machinery, are maintained in mature transcripts. IR is a widespread mechanism affecting many different genes with cell type-specific patterns. We hypothesized that, in an RNA-seq experiment, reads derived from retained introns can introduce a bias in the detection of overlapping, independent TEs RNA expression. In this study we performed meta-analysis using public RNA-seq data from lymphoblastoid cell lines and show that IR can impact TEs quantification using established tools with default parameters. Reads mapped on intronic TEs were indeed associated to the expression of TEs and influence their correct quantification as independent transcriptional units. We confirmed these results using additional independent datasets, demonstrating that this bias does not appear in samples where IR is not present and that differential TEs expression does not impact on IR quantification. We concluded that IR causes the over-quantification of intronic TEs and differential IR might be confused with differential TEs expression. Our results should be taken into account for a correct quantification of TEs expression from RNA-seq data, especially in samples in which IR is abundant.

Dataset Information

Differential Expression Analysis of Nothobranchius furzeri Transposable Elements from RNA-seq Data.

Publications

Differential Expression Analysis of <i>Nothobranchius furzeri</i> Transposable Elements from RNA-seq Data.

Similar Datasets

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