Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

MicroRNAs (miRNAs) are a class of endogenous small non-coding RNA molecules that regulate post-transcriptional expression of target genes and play important roles in animal development. The objectives of this study were to characterize the egg miRNA transcriptome and identify novel egg-predominant miRNAs in rainbow trout. Small RNAs isolated from mature unfertilized rainbow trout eggs were subjected to deep sequencing using an Illumina Genome Analyzer. The massive sequencing produced 24,621,741 quality reads, among which, 266 known miRNAs were identified and 230 putatively novel miRNAs were predicted. The most abundantly known miRNAs are let-7 and miR-21, accounting for 24.06% and 18.71% of the known miRNAs, respectively. Other known miRNAs which are abundantly present in eggs include miR-24, miR-202, miR-148, miR-30, miR-10, miR-146, miR-25, and miR-143. Real time PCR analysis using cDNAs derived from 10 tissues validated 87 out of 90 selected putative miRNAs and identified three novel miRNAs predominantly expressed in rainbow trout eggs. Each of these novel egg-predominant miRNAs is predicted to target a significant number of genes, most of which are significantly down-regulated in naturally ovulated rainbow trout eggs based on analysis of publicly available microarray data sets. Quantitative real time PCR analysis also demonstrated low expression of a selected number of target genes in eggs relative to liver and muscle tissues. This study represents the first complete survey of miRNAs in fish eggs and provides a starting point for future studies aimed at understanding the roles of miRNAs in controlling egg quality and early embryogenesis in rainbow trout.

Genetic improvement for enhanced disease resistance in fish is an increasingly utilized approach to mitigate endemic infectious disease in aquaculture. In domesticated salmonid populations, large phenotypic variation in disease resistance has been identified but the genetic basis for altered responsiveness remains unclear. We previously reported three generations of selection and phenotypic validation of a bacterial cold water disease resistant line of rainbow trout, designated ARS-Fp-R. This line has higher survival after infection by either standardized laboratory challenge or natural challenge as compared to two reference lines, designated ARS-Fp-C (control) and ARS-Fp-S (susceptible). In this study, we utilized 1.1 g fry from the three genetic lines and performed RNA-seq to measure transcript abundance from the whole body of naive and Flavobacterium psychrophilum infected fish at day 1 (early time-point) and at day 5 post-challenge (onset of mortality). Sequences from twenty-four libraries were mapped onto the rainbow trout genome reference transcriptome of 46,585 predicted protein coding mRNAs that included 2,633 putative immune-relevant gene transcripts. A total of 1,884 genes (4.0% genome) exhibited differential transcript abundance between infected and mock-challenged fish (FDR<0.05) that included chemokines, complement components, tnf receptor superfamily members, interleukins, nod-like receptor family members, and genes involved in metabolism and wound healing. The largest number of differentially expressed genes occurred on day 5 post-infection between naive and challenged ARS-Fp-S line fish correlating with high bacterial load. After excluding the effect of infection, we identified twenty-one differentially expressed genes between the three genetic lines. In summary, these data indicate global transcriptome differences between genetic lines of naive animals as well as differentially regulated transcriptional responses to infection.