An RNA‐Seq‐based reference transcriptome for Citrus

Abstract

Summary Previous RNA‐Seq studies in citrus have been focused on physiological processes relevant to fruit quality and productivity of the major species, especially sweet orange. Less attention has been paid to vegetative or reproductive tissues, while most Citrus species have never been analysed. In this work, we characterized the transcriptome of vegetative and reproductive tissues from 12 Citrus species from all main phylogenetic groups. Our aims were to acquire a complete view of the citrus transcriptome landscape, to improve previous functional annotations and to obtain genetic markers associated with genes of agronomic interest. 28 samples were used for RNA‐Seq analysis, obtained from 12 Citrus species: C. medica, C. aurantifolia, C. limon, C. bergamia, C. clementina, C. deliciosa, C. reshni, C. maxima, C. paradisi, C. aurantium, C. sinensis and Poncirus trifoliata. Four different organs were analysed: root, phloem, leaf and flower. A total of 3421 million Illumina reads were produced and mapped against the reference C. clementina genome sequence. Transcript discovery pipeline revealed 3326 new genes, the number of genes with alternative splicing was increased to 19 739, and a total of 73 797 transcripts were identified. Differential expression studies between the four tissues showed that gene expression is overall related to the physiological function of the specific organs above any other variable. Variants discovery analysis revealed the presence of indels and SNPs in genes associated with fruit quality and productivity. Pivotal pathways in citrus such as those of flavonoids, flavonols, ethylene and auxin were also analysed in detail.

Comparative transcriptome analysis of Poncirus trifoliata identifies a core set of genes involved in arbuscular mycorrhizal symbiosis

“…The resulting clean reads were then aligned to the Citrus sinensis reference genome ( Xu et al , 2013 ) ( http://citrus.hzau.edu.cn/orange ) using the software SOAPaligner/SOAP2 ( Li et al , 2009 ) allowing two bp mismatches per read. We note that this approach excluded genes that are not present in the C. sinensis genome, or which are divergent between C. sinensis and P. trifoliata / C. trifoliata ; nevertheless, approximately 74% of the RNA-seq reads could be mapped, which was similar to previous RNA-seq analyses for Poncirus ( Terol et al , 2016 ; Chen et al , 2017 ; Supplementary Table S1 ). Gene expression levels were calculated using the RPKM (reads per kb per million reads) method as follows: RPKM = 10 6 × [(Number of reads uniquely aligned to gene A)/(Total number of reads that uniquely aligned to all genes)] × [(Number of bases of gene A)/10 3 ] ( Mortazavi et al , 2008 ).…”

Mechanistic insights into longan (Dimocarpous longan Lour.) transcriptome for physiological characterization for defensive genes and differential gene expression analysis with longan embryogenic callus transcriptome

“…In the current study only CLC Genomics Workbench was used for sequence assembly since many previous studies have been demonstrated that CLC genomics workbench is most efficient tools based on different assessment parameters specifically N50 length and average contig length (Misner et al, 2013). Transcritome assembly for various organisms including Cleome spinosa and Cleome gynandra (Brautigam et al, 2011), Amycalatopsis orientalis (Jeong et al, 2013), Dodonaea viscosa (Christmas et al, 2015) and 12 Citrus Species (Terol et al, 2016) have already been done using same approach.…”

Transcriptome Analysis of Bael (Aegle marmelos (L.) Corr.) a Member of Family Rutaceae

“…Earlier studies have pointed out a higher metabolic activity in the leaves of A. marmelos, which is because of the presence of phytochemicals such as alkaloids, flavonoids, and phenols [47,48]. We have identified a number of GO terms in the leaves of A. marmelos; this information could lead to the identification of important pathways of metabolic compounds in A. marmelos [49].…”