Long-non coding RNAs repertoires in liver and two T lymphocyte cell types in four livestock species

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Muret, Kévin | Djebali Quelen, Sarah | Derrien, Thomas | Cabau, Cédric | Klopp, Christophe | Esquerre, Diane | Munyard, Kylie | Tosser-Klopp, Gwenola | Acloque, Hervé | Giuffra, Elisabetta | Foissac, Sylvain | Lagarrigue, Sandrine

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Understanding genome-to-phenome relationships requires deep and cross-disciplinary genetic analyses among which functional annotation provides crucial insights. The development of High Throughput Sequencing and RNA-seq now help us to find a large number of heterogeneous and low-expressed transcripts known to be long non-coding RNAs (lncRNAs). One of the aims of the FAANG pilot project ‘FR-AgENCODE’ is to identify and characterise the long non-coding RNAs of multiple tissues and cell lines in 4 farm animals (chicken, bovine, pig and goat) of both sexes. Here, we focus our analysis on the liver tissue and two blood T-cell types (CD3+CD4+, CD3+CD8+) where samples were collected through 4 biological replicates (2 males and 2 females). It allows us to compare lncRNA repertoires between tissues, sex and species in relation with fundamental biological functions like energy storage and immunity. High depth strand-specific RNA-seq produced ~200Mpaired-end reads for each of the 16 RNA-seq datasets. After transcriptome reconstruction, we used the recently published FEELnc program (Wucher et al., 2017, Nucleic Acid Research) to identify lncRNAs longer than 200 bp and without protein-coding capabilities. FEELnc also classifies lncRNAs based on their genomic localizationswith respect to the ENSEMBL protein-coding annotation: intergenic lncRNAs are categorized depending on the distance and orientation with respect to the closest mRNAs and the intragenic lncRNAs are extracted based on their overlap with mRNAs exons and introns. We will report these lncRNA repertoires in terms of intergenic/ intragenic lncRNA class, structure and expression and comparing these features between livestock species, tissues and sexes. By profiling the transcriptional landscape of lncRNAs in these 4 species, this data will further contribute to the global action for annotating functional elements of livestock genomes.

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