BMC Genomics 2009, 10:58 http://www.biomedcentral.com/1471-2164/10/58 J2 stage probe. The remainder of hatched infective J2 was divided among the forty pots for seedling inoculation. Four days after infection, parasitic J2 were collected from twelve pots. Eight days after infection, another twelve pots were harvested for collection of J3 juveniles and fourteen days after infection, a further ten pots were used to collect J4 juveniles. Finally, twenty-one days after infection, the final six pots were harvested for collection of adult females. RNA extraction and GeneChip hybridization All data analyzed here is based on our previous results [35]. For that study, frozen nematode tissue was disrupted with frozen zirconia beads (BioSpec Products, Bartlesville, OK) in a beadbeater (BioSpec Products, Bartlesville, OK). RNA was isolated using the Versagene kit (Gentra Systems, Minneapolis, MN) as described [35]. RNA quality and concentration of each sample were determined by RNA Nanochip on a 2100 Bioanalyzer (Agilent Technologies Inc, Palo Alto, CA) and by a NanoDrop spectrophotome- ter (NanoDrop Technologies, Wilmington, DE). Standard procedures for reverse transcription and labeling of the probes and for hybridization and scanning of the Gene- Chips were followed by the Iowa State University Gene- Chip Facility. Design of microarray experiments and GeneChip data analysis and validation For our previous study [35], from which the microarray data analyzed here has been drawn, we measured expres- sion using 18 Affymetrix Soybean Genome Array Gene- Chips (3 replications × 6 life stages) using a randomized complete block design with replications as blocks. In that study, the Affymetrix signal data were transformed with the natural log (ln) and normalized by median centering prior to the analysis. The normalized data for each gene were analyzed separately using a standard linear model with fixed effects for replications and stages. To test for a difference in expression between life stages for each probeset, we performed F tests, which resulted in p-values. A q-value was calculated for each p-value following [52] and was used to maintain approximate control of the false discovery rate (FDR) at 5% by declaring q-values at or below 0.05 significant. For a detailed description of bio- logical sample preparation, RNA extraction and experi- mental design see our previous study [35] and [53]. Clustering was implemented to group the observed expression patterns of differentially expressed genes. We estimated the mean normalized expression level for each probeset in all six life stages. The resulting six estimated values were standardized to have mean 0 and standard deviation 1 within each probeset. The Euclidian distance between any pair of standardized expression profiles was used as a measure of dissimilarity in all clustering algo- rithms. This approach considers genes with similar expres- sion patterns to be close in six-dimensional space and is equivalent to using (1-r)0.5 as the measure of dissimilarity, where r is the Pearson correlation coefficient between non-standardized expression profiles. All clusters and related figures were generated using the free open-source statistical software package R. Hierarchical agglomerative clustering using average linkage to measure the dissimilar- ity between clusters was carried out using the R function hclust from the R cluster library. The microarray data used here has been validated in our previous study [35] by quantitative real-time PCR (qRT- PCR). For that study, we examined the expression patterns of six genes representing different expression patterns for each of the five consecutive pairs of life stages (egg/infec- tive J2, infective J2/parasitic J2, parasitic J2/J3, J3/J4, J4/ female), totaling thirty different genes. The template used for the qRT-PCR was the same biological material used for the microarray hybridizations, and the reactions were per- formed in technical triplicates. As detailed in [35], we found qualitative agreement for 28 out of 30 tested genes between our GeneChip and qRT-PCR results. Identification of signal peptide-encoding H. glycines genes The nucleotide consensus sequences of all 7,530 H. gly- cines probesets (freely available at Affymetrix [54]) were translated into the three forward reading frames (for sense or S1 probesets) or into the three reverse reading frames (for antisense or A1 probesets). Additionally, all nucle- otide probeset consensus sequences were translated beginning with the first and second start codons, so that for each probeset up to five translations were generated. All translations were then analyzed for the presence of a signal peptide using SignalP 3.0 [55]. Only those transla- tions were kept for which the C-score, Y-max, S-max, S- mean and D-score of the SignalP neural network output were positive and for which the SignalP hidden Markov model predicted a signal peptide. We then filtered these translations and kept only those that had at least thirty amino acids after the predicted signal peptide cleavage site. The signal peptides of these translations were cleaved off and the remaining amino acid sequences were checked for the presence of transmembrane helices with the TMHMM software [56]. All translations for which a trans- membrane helix was predicted were removed. Probeset nucleotide consensus translations that passed this identi- fication protocol were sorted into probesets, from which a final number of genes was determined by taking probeset variants as designed by Affymetrix into account. Identification of cross-hybridizing soybean and P. sojae probesets Soybean and P. sojae probesets were considered as cross- hybridizing if their respective signal intensities were called Page 13 of 17 (page number not for citation purposes)