Gene-chip based data analysis suggested that a subset of VvWRKYs was activated in respond to diverse biotic and abiotic stresses.
However, we have shown that comparative WGS with gene-by-gene-based data analysis (MLST+) is capable of discriminating L. monocytogenes SV1/2b clones grouped together by the genotyping methods PFGE (AscI, ApaI, and SmaI) and fAFLP (HhaI and HindIII).
A comparative analysis of qRT-PCR results, gene-chip based data and transcriptome analysis allowed us to identify 15 VvWRKYs that show identical expression patterns during cold treatment at least in two kinds of analyses.
In microarray gene expression analysis, ratio based data normalization and selection of features were performed using Acuity 4.0 (Molecular Devices).
We chose a panel of five irradiation-responsive genes (FDXR, CDKN1A, SESN1, BBC3, PHPT1) previously evaluated by Amundson et al., as well as nine genes based on our data analysis [ 7].
These novel putative oncogenes and tumor suppressor genes based on our data analysis include 20 genes (by q-value < 0.20), which previously have not been associated with ESFT (Table 3).
To investigate the impact from ubiquitin fusion on the expression of hG-CSF, three individual plants carrying a single copy of target gene (based on Southern blot analysis, data not shown) were selected from SH and USH transgenic plants, respectively, and real-time PCR (RT-PCR) was used to quantify the relative expression of hG-CSF at steady-state mRNA level.
RG1 through RG9 were selected as potentially stable genes based on analysis of RNA-Seq data performed on R. opacus grown in a minimal salts medium with either glucose or phenol3.
This is typically achieved by selecting a set of genes based on the analysis of experimental data, annotation database, or literature-extracted information on protein-protein regulation network [ 13, 14, 27].
These conclusions are based on Cytb and RAG2 gene data; analysis with additional markers would greatly enhance our understanding of this species.
The design of the modified SAG1 gene was based on the data analysis of codon usage.
