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A bioinformatic analysis of previously published RNA-Seq studies on Huanglongbing (HLB) response and tolerance in leaf tissues was performed. The aim was to identify genes commonly modulated between studies and genes, pathways and gene set categories strongly associated with this devastating Citrus disease. Bioinformatic analysis of expression data of four datasets present in NCBI provided 46-68 million reads with an alignment percentage of 72.95-86.76%. Only 16 HLB-regulated genes were commonly identified between the three leaf datasets. Among them were key genes encoding proteins involved in cell wall modification such as CESA8, pectinesterase, expansin8, expansin beta 3.1, and a pectate lyase. Fourteen HLB-regulated genes were in common between all four datasets. Gene set enrichment analysis showed some different gene categories affected by HLB disease. Although sucrose and starch metabolism was highly linked with disease symptoms, different genes were significantly regulated depending on leaf growth and infection stages and experimental conditions. Histone-related transcription factors were highly affected by HLB in the analyzed RNA-Seq datasets. HLB tolerance was linked with induction of proteins involved in detoxification. Protein-protein interaction (PPI) network analysis confirmed a possible role for heat shock proteins in curbing disease progression.
Pubmed ID: 29541089
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View all literature mentionsSoftware that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries.
View all literature mentionsTool that extracts and displays alternative splicing (AS) events from a given genomic annotation of exon-intron gene coordinates. By comparing all given transcripts, it detects the variations in their splicing structure and identifies all AS events (like exon skipping, alternate donor, etc) by assigning to each of them an AS code. It provides a visual summary of the AS landscape in the analyzed dataset, the possibility to browse the results on the UCSC website or to download them in GTF or ASTA format. You can use AStalavista for any genome by providing your own annotation set, the identifier of your gene(s) of interest, or analyze the AS landscape of reference annotation datasets like Gencode, RefSeq, Ensembl, FlyBase, etc.
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View all literature mentionsProbabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq and identifies differentially regulated isoforms or exons across samples.
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View all literature mentionsGraph-based alignment of next generation sequencing reads to a population of genomes.
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