|Application:||Gene expression microarray analysis|
|Number of samples:||8|
|Release date:||Jul 31 2011|
|Last update date:||Mar 23 2012|
|Dataset link||Microarray evidence for off target effects and the optimisation of targeted RNAi mediated gene silencing in the cattle tick Rhipicephalus (Boophilus) microplus|
The microarray was conducted by NimbleGen Systems Inc following the method reported by Saldivar [Saldivar L et al., Insect Mol Biol 2008, 17(6):597-606]. Briefly, Custom high-density single channel oligonucleotide arrays were constructed by NimbleGen Systems Inc. using 13 601 of the 13 643 members of BmiGI Version 2 with 14 perfect match 50-mer probes per BmiGI target. No mismatched probes were included on the arrays, although probes with randomly generated sequences were included. These random sequence probes were designed to match the melting temperature (Tm) of the other probes on the array and to reflect the distribution of non-specific signal intensities for binding events to probes with approximately the same composition as the perfect match probes but with random sequences. The associated GEO dataset can be found online at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6373. Each microarray chip includes two in-slide replicates (spot replicates). In this experiment two biological replicates for each sample were hybridized to assess variability between individual samples. Each pooled sample consisted of 30 female adult ticks (N strain) as the source of RNA. Four technical replicates were processed, i.e. repeated measurements of the same pooled R. microplus mRNA, consisting of two chip replicates with two spot replicates. The chip replicates were hybridized on different dates to different chips, while spot replicates indicate the same probe was spotted on different locations on the chip. Four R. microplus samples were hybridized to separate microarrays at NimbleGen Systems Inc. (Madison, WI, USA). Data Processing: Statistical analysis Following array hybridization the raw intensity values were background corrected using convolution and normalized using quantile normalization to adjust for technical sources of variation. Final log2 expression intensities were generated using the Robust Multichip Average (RMA) algorithm [Irizarry et al.,Biostatistics 2003, 4:249-264]; [Groeneveld E, et al.,Proceedings 6th World Cong Genet Appl Livest Prod,: 1998; Armidale, NSW, Australia; 455-458] implemented in R. Differential expression was tested on the RMA normalized intensities using a mixed model of the form yijkr=μ+BSrk+Gi+GTij+Eijkr where yijkr are the log2 RMA normalized signal intensities; i =gene, j=treatment, r=block, k=slide/array, the main fixed effect is BSrk (block by slide interaction), Gi is the main random effect of the gene; GTij is the random interaction term of the gene by treatment; E is just the error term; and normal assumptions for the random effects – iid are assumed. The model was fitted using VCE4.0 [Groeneveld E, et al., 1998]. Differentially expressed (DE) probes were considered as those which were three or more standard deviations away from the mean (p-value 1.5 and p-value <0.001 were screened against the following databases on the CCG [Centre for Comparative Genomics: http://ccg.murdoch.edu.au], Grendel HPC system [Hunter A, et al.,Australasian Workshop on Grid Computing and e-Research 2005:2-3]; NCBI protein (nr and patent) [National Centre for Biotechnology Information: http://www.ncbi.nlm.nih.gov], String [Mering von C., et al.,Nucleic Acids Research 2006:00: D01-D05], COG [Tatusov R, et al., BMC Bioinformatics 2003, 4], tigr_bmigi.062608 [Wang et al.,BMC Genomics 8:368], NCBI Conserved Domain database [Goonesekere N, et al., Proteins 2008, 71(2):910-919], and KEGG (Kanehisa & Goto 2000 Nucleic Acids Res. 28, 27-30; Kanehisa et al 2006 Nucleic Acids Research 34, D354-D357; Kanehisa et al 2010 Nucleic Acids Research 38, D355-D360). Further analysis of differentially expressed transcripts identified in the microarray experiments was undertaken using the BLAST program suite (Altschul et al., 1997) for similarity searches in the UniProt database (TheUniProtConsortium, 2010 Nucleic Acids Res 38, D142-D148.) and in the protein sequence database of Ixodes scapularis tick (available from http://www.vectorbase.org/Ixodes_scapularis/Info/Index; last accessed 2010-11-21). The results of the blastx searches against the I. scapularis protein sequences where used to map the R. microplus ESTs to unique identifiers in the DAVID Bioinformatics Resource and perform a subsequent functional annotation to Gene Ontology terms in the categories “Biological Process (BP)”, “Cellular Component (CC)”, and “Molecular Functions (MF)” (Dennis et al 2003 Genome Biology 4: P3; Huang et al 2009 Nature Protoc. 4: 44-57.).