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[…] was accessed using a model 2100 Bioanalyzer (Agilent) and a High Sensitivity DNA kit (Agilent). The accurate quantification of the libraries was accomplished using model 7500 real-time PCR (Applied Biosystems) and a Kapa library quantification kit (Kapa Biosystems). Paired-end sequencing (2 × 250 bp) was performed on a MiSeq sequencer (Illumina) for the following numbers of replicates: control 24 h = 2, inoculated 24 h = 3, control 48 h = 2, inoculated 48 h = 2, control 72 h = 3, inoculated 72 h = 3., The sequences were preprocessed to trim poly(A-T) tails that were at least 20 bp long, to remove reads shorter than 35 bp, and to trim sequences with a quality score lower than Phred 30, using Prinseq software (). The processed sequences from all of the samples were assembled using Trinity software, and sequences larger than 199 bp were used in the downstream analysis. Sequences from each sample were mapped against the assembled reads using Bowtie 2 () (with the following parameters: --end-to-end; --no-mixed; --no-discordant; --score-min L,-0.1,-0.1) and were clustered into genes using Corset software (minimum read count = 5) (). A few bacterial sequences were detected through Blast searches against the NCBI-nr database and were removed from subsequent analysis. Statistically relevant genes differentially expressed between the control and the inoculated samples were identified using the edgeR software package associated with the Fisher exact test and Bonferroni correction for multiple tests, considering the following parameters: corrected P value, ≤0.001; log fold change [logFC] value, ≥2.0 (). To plot a heat map of gene expression levels comparing control and inoculated samples (), we used Z score analysis, a conventional method of data normalization that calculates the mean expression value for a gene under the different conditions and normalizes the deviations as a function of the mean. The differentially expressed genes were annotated through a Blast search against the NCBI-nr database (E value, <10−5), and GO terms were assigned using the Blast2go tool (). To identify the transcripts associated with the biosynthesis of terpenoid compounds, we analyzed the transcriptome of L. dendroidea using hidden Markov models generated from the alignment of sequences available in the KEGG database through the use of HMMER 3.0 software (), following the method previously used by de Oliveira et al. (). The sequences matching these profiles were annotated through a Blast search against the NCBI-nr, PlantCyc, and Uniprot databases. The functional identifications were manually confirmed., This paper is part of the DSc requirements of Louisi Souza de Oliveira at the Biodiversity and Evolutionary Biology Graduate Program of the Federal University of Rio de Janeiro., This research received the financial support of CAPES, CNPq, and FAPERJ. F.L.T. and R.C.P. thank CNPq for their Research Productivity Fellowships. The funders had no role in study design, data collection and interpretation, or the decision to submi […]

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