Dataset features


Application: RNA-seq analysis
Number of samples: 24
Release date: Nov 19 2018
Last update date: Nov 19 2018
Access: Public
Taxon: Glycine max, Sclerotinia sclerotiorum
Dataset link Global transcriptome analysis of resistant and susceptible soybean lines following Sclerotinia sclerotiorum infection

Experimental Protocol

Four-week-old soybean plants of both the resistant (R) and the susceptible (S) were inoculated using the cut petiole inoculation method as described by (Ranjan, 2017; MPP., 19(3):700-714). Plant tissue was sampled by cutting horizontally above and below (1.5 cm) the node of the first trifoliate with a clean straight-edge razor. Tissue samples were collected at 24, 48, and 96 h post inoculation (hpi) and immediately frozen in liquid nitrogen prior to RNA extraction. The stem tissue from non-inoculated (0 h) plants were also collected as a control. The experimental design was completely randomized and consisted of three biological replicates for each of the treatments. For each biological replicate, stem segments (~3 cm, first internode) from two different plants were pooled together. Soybean seedlings and plants were maintained in the greenhouse or growth chamber at 24 ± 2◦C with 16 h light/8 h dark photoperiod cycle. Total RNA extracted for each sample was randomly fragmented, and individually indexed libraries were prepared using the TruSeq RNA Sample Preparation v2 kit according to the manufacturer’s instructions and were sequenced using Illumina HiSeq2500. Raw sequence reads were mapped to both genomes using the Subjunc aligner from Subread. Alignments were compared to the gene annotation GFF files for both organisms (Soybean: Gmax_275_Wm82.a2. v1. gene. gff3 (Schmutz, Cannon et al. 2010), S. sclereotiorum: sclerotinia_sclerotiorum_2_transcripts.gtf (Amselem, Cuomo et al. 2011) and raw counts for each gene were generated using the feature Counts tool from subread. The raw counts data were normalized using voom from the R Limma package, then used to generate differential expression (logFC) values.










Mehdi Kabbage