Computational protocol: Validating Internal Control Genes for the Accurate Normalization of qPCR Expression Analysis of the Novel Model Plant Setaria viridis

Similar protocols

Protocol publication

[…] Candidate genes for qPCR normalization were selected based on previous studies conducted on maize [] and rice [] (). The nucleotide sequences of the 15 candidate genes, and an additional two target genes, were BLAST searched against the Phytozome database (http://phytozome.jgi.doe.gov/) to identify homologues in the S. italica genome (v2.1), which is a close relative of S. viridis. Sequences with e-values lower than 10E-40 were selected for primer design. The homology was confirmed based on alignment with the transcriptome sequencing of S. viridis leaves (; personal communication with Todd Mockler). The S. viridis sequences are not available on public DNA databases, therefore the accession numbers are not shown on .Primer3 [] was used to design the primers, which were constructed to be 20–22 bp in length and to have melting temperatures of approximately 60°C and GC contents ranging between 35–65%. The amplicons varied from 80 to 180 bp in length and were designed to span intronic regions whenever possible. The specificity of the primers was validated both in silico and in the RT-PCR profile (). More detailed information about the candidate genes and primers that were used in this study is available in . [...] Both the efficiency of each experimental set and the Cq values that were generated for each qPCR reaction were estimated using the Miner software program []. This algorithm employs a three-parameter simple exponential non-linear regression to determine the amplification efficiency that was achieved during each cycle. The average amplification efficiency of each gene was used to calculate the non-normalized expression values, which did not depend on a standard curve. When determining Cq values, the Miner software applies the first positive second derivative maximum to find the beginning of each exponential phase (thus the Cq).The non-normalized expression values were calculated by the qBase v1.3.5 software program [] using the formula Q = E ΔCq Q = EΔCq, in which E represents the efficiency of gene amplification and ΔCq is the difference between the sample with the lowest expression in the dataset minus the Cq value of the sample analyzed. These quantities were then imported into the geNorm [] and NormFinder [] analysis tools, which were used according to the directions described in their manuals.To determine the relative expression of the target genes, Cq and efficiency values were submitted to qBase, as described above; however, for the purpose of normalization, two reference genes were selected and their relative expression levels were calculated based on the ΔΔCq model []. […]

Pipeline specifications

Software tools Primer3, NormFinder
Databases Phytozome
Applications RNA-seq analysis, qPCR
Organisms Setaria viridis
Chemicals Carbon