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Gene prediction workflow. (A) RNAseq samples are aligned on the reference genome. (B) Biological replicate alignments are merged together into 64 different datasets. Transcript reconstruction was performed independently on each dataset using three different programs: Cufflinks, Scripture and Isolasso. The Venn diagram shows the percentage of reconstructed transcripts in common among the three software while the numbers between brackets indicates the average number of reconstructed transcripts per sample. We selected only those transcript models predicted by at least two programs and with a length higher than 150 bases. (C) The selected transcripts were assembled using PASA software. (D) PASA assemblies were used to update v1 gene predictions. (E) A new gene prediction was performed integrating with EvidenceModeler (EVM) software different sources of evidence such as PASA transcripts, ESTs and proteins alignments and Augustus prediction trained with PASA assemblies. The produced gene set was compared to v1 gene prediction and only the new gene loci were selected for further analysis. After applying different filtering criteria, we obtained a final dataset of 2,258 new genes. (F) The final v2 gene prediction integrates genes generated by the steps described in D (v1 update) and E (new gene prediction).

Gene prediction workflow. (A) RNAseq samples are aligned on the reference genome. (B) Biological replicate alignments are merged together into 64 different datasets. Transcript reconstruction was performed independently on each dataset using three different programs: Cufflinks, Scripture and Isolasso. The Venn diagram shows the percentage of reconstructed transcripts in common among the three software while the numbers between brackets indicates the average number of reconstructed transcripts per sample. We selected only those transcript models predicted by at least two programs and with a length higher than 150 bases. (C) The selected transcripts were assembled using PASA software. (D) PASA assemblies were used to update v1 gene predictions. (E) A new gene prediction was performed integrating with EvidenceModeler (EVM) software different sources of evidence such as PASA transcripts, ESTs and proteins alignments and Augustus prediction trained with PASA assemblies. The produced gene set was compared to v1 gene prediction and only the new gene loci were selected for further analysis. After applying different filtering criteria, we obtained a final dataset of 2,258 new genes. (F) The final v2 gene prediction integrates genes generated by the steps described in D (v1 update) and E (new gene prediction).

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Alternative splicing (AS) significantly enhances transcriptome complexity. It is differentially regulated in a wide variety of cell types and plays a role in several cellular processes. Here we describe a detailed survey of alternative splicing in grape based on 124 SOLiD RNAseq analyses from different tissues, stress conditions and genotypes. We u...

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... assembly was achieved using hisat2 with default settings and the -sensitive option (Kim et al. 2015). This is followed by gene count summarization using FeatureCounts (Liao et al. 2014) and the CRIBI V2 prediction (Vitulo et al. 2014) as described previously. Using edgeR (Robinson et al. 2010) with the quasi-likelihood F-test method in R (https://cran.r-project.org/), ...
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... Transcriptome data analysis has revealed that at least 44 (12.4%) of 354 high-confidence miRNA binding sites in Arabidopsis are affected by AS (Yang et al. 2012). (Vitulo et al. 2014) Research suggests that this process may also play a role in plant responses to osmotic adjustment, including drought, as indicated by the 139 grapevine genes with miRNA target sites affected by AS. AS events have been observed in both grape (Mica et al. 2010) and Arabidopsis (Hirsch et al. 2006), indicating an additional level of regulatory control over miRNAtargeted genes. ...
... Transcriptome data analysis has revealed that at least 44 (12.4%) of 354 high-confidence miRNA binding sites in Arabidopsis are affected by AS (Yang et al. 2012). (Vitulo et al. 2014) Research suggests that this process may also play a role in plant responses to osmotic adjustment, including drought, as indicated by the 139 grapevine genes with miRNA target sites affected by AS. AS events have been observed in both grape (Mica et al. 2010) and Arabidopsis (Hirsch et al. 2006), indicating an additional level of regulatory control over miRNAtargeted genes. ...
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