Computational protocol: Comparative transcriptome analysis of axillary buds in response to the shoot branching regulators gibberellin A3 and 6-benzyladenine in Jatropha curcas

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[…] Low-quality reads with Phred scores < 20 were trimmed using Fastq_clean, and the data quality was assessed using FASTQC. The filtered reads were assembled using Trinity (version 2.0.6) with default parameters, . The filtered reads from each library were mapped to de novo assemblies using Bowtie version v1.1.1 by allowing two mismatches (-v 2 -m 10). The transcript abundance was estimated using Corset (version 1.03). The count data generated from Corset were processed using the edgeR package. Transcripts with less than one count per million reads (CPM) for at least three libraries were removed, and the remaining data were used for the next analysis. A matrix was constructed using the single factor style. Effective library sizes were determined using the trimmed mean of M values (TMM) normalization method. The common dispersion and tag wise dispersion were estimated using the quantile-adjusted conditional maximum likelihood (qCML) method. A multidimensional scaling was performed through the “plotMDS.dge” function of the edgeR package. The exact test was performed to compute the expression of genes between the treatment and mock groups. Raw P values were adjusted for multiple testing using a false discovery rate (FDR). Genes with an FDR ≤ 0.05 and fold change (FC) ≥ 2 were regarded as differentially expressed genes (DEGs). GO analysis of the DEGs and pathways were processed using the DAVID with a cutoff of P-value ≤ 0.01. Hierarchical clustering of the co-regulated genes listed in Table  was performed using the pheatmap R package (version 1.0.7). […]

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