Transcriptome studies in citrus have been mostly focused on the characterization of physiological processes of high relevance to fruit quality and productivity, especially of sweet orange, as it is the most important citrus fruit for the juice industry. Deep sequencing of the transcriptome has also been applied for the identification of candidate genes in processes of agronomical interest (Canales et al., 2014 Chen et al., 2013 Venu et al., 2011), or to obtain markers for large scale genotyping (Haseneyer et al., 2011 Scaglione et al., 2012). This approach has been greatly used to improve functional annotation of model plants like Arabidopsis (Filichkin et al., 2010 Ossowski et al., 2008), rice (Lu et al., 2010 Mizuno et al., 2010) and poplar (Ko et al., 2012), with outstanding results. The analysis of the transcriptome is a crucial step to characterize any species genome, and during the past years, these studies have been boosted by the development of RNA-Seq technique (Egan et al., 2012 Wang et al., 2009). Lineages that gave rise to the most modern cultivars are still under discussion (Nicolosi et al., 2000). Citrus fruits have been traditionally classified into different groups based on the use of molecular markers, although the phylogeny of the species is not yet clear due to the presence of numerous hybrids. aurantium) was obtained and compared (Wu et al., 2014). maxima), sweet orange ( C. sinensis) and sour orange ( C. clementina, as well as the genome sequences of mandarin ( C. reticulata), pummelo ( C. Many efforts have been carried out to characterize the genome sequence of the main Citrus species: the draft genome of sweet orange, Citrus sinensis, was released in 2012 (Xu et al., 2013) more recently a high-quality reference genome sequence of a haploid clementine, C. Pivotal pathways in citrus such as those of flavonoids, flavonols, ethylene and auxin were also analysed in detail.Ĭitrus, including species such as sweet orange, mandarin, lemon or grapefruit, is one of the most important fruit crops in the world, both in terms of fruit production and economical value. Variants discovery analysis revealed the presence of indels and SNPs in genes associated with fruit quality and productivity. 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.
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. A total of 3421 million Illumina reads were produced and mapped against the reference C. clementina genome sequence. Four different organs were analysed: root, phloem, leaf and flower. 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. 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. In this work, we characterized the transcriptome of vegetative and reproductive tissues from 12 Citrus species from all main phylogenetic groups. Less attention has been paid to vegetative or reproductive tissues, while most Citrus species have never been analysed. the oligo pair-annealing score." Not very helpful in my view.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. the secondary structure score and parameters pertaining to oligo-pairs such as e.g. The algorithm considers both the parameters pertaining to single oligos, such as e.g. (Edit: From the help menu: "CLC Main Workbench employs a proprietary algorithm to rank primer and probe solutions.
#Clc genomics workbench citation free
However, since you are quite free to set a multitude parameters, I guess that the hits are scored mainly based on fitting these as well as the alignment with the homolog sequences. I didn't really get any insight into the algorithm that CLC uses to determine primers in the manual.
#Clc genomics workbench citation software
While checking the software options I saw that most people recommend Primer3 in this context. However, I didn't use it for TaqMan probes yet. It offers convienient options to design primers from alignments that are able to discriminate between rather homolog sequences. Since I am used to do primer design in the CLC Main Workbench that would obviously be my first choice. I want to design primers/probes for a TaqMan application.
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