Current status and future perspectives for sequencing livestock genomes

Only in recent years, the draft sequences for several agricultural animals have been assembled. Assembling an individual animal＇s entire genome sequence or specific region（s） of interest is increasingly important for agricultura researchers to perform genetic comparisons between animals with different performance. We review the current status for several sequenced agricultural species and suggest that next generation sequencing （NGS） technology with decreased sequencing cost and increased speed of sequencing can benefit agricultural researchers. By taking advantage of advanced NGS technologies, genes and chromosomal regions that are more labile to the influence of environmental factors could be pinpointed. A more long term goal would be addressing the question of how animals respond at the molecular and cellular levels to different environmental models （e.g. nutrition）. Upon revealing important genes and gene-environment interactions, the rate of genetic improvement can also be accelerated. It is clear that NGS technologies will be able to assist animal scientists to efficiently raise animals and to better prevent infectious diseases so that overall costs of animal production can be decreased.

Correlating Bladder Cancer Risk Genes with Their Targeting MicroRNAs Using MMiRNA-Tar

The Cancer Genome Arias （TCGA） （http：//cancergenome.nih.gov） is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating microRNAs. We developed a web interface tool MMiRNA-Tar （http：f/bioinfl.indstate.edufMMiRNA-Tar） that can calculate and plot the correlation of expression for mRNA-microRNA pairs across samples or over a time course for a list of pairs under different prediction confidence cutoff criteria. Prediction confidence was estab- lished by requiring that the proposed mRNA-microRNA pair appears in at least one of three target prediction databases： TargetProfiler, TargetScan, or miRanda. We have tested our MMiRNA-Tar tool through analyzing 53 tumor and 11 normal samples of bladder urothelial carcinoma （BLCA） datasets obtained from TCGA and identified 204 microRNAs. These microRNAs were correlated with the mRNAs of five previously-reported bladder cancer risk genes and these selected pairs exhib- ited correlations in opposite direction between the tumor and normal samples based on the cus- tomized cutoff criterion of prediction. Furthermore, we have identified additional 496 genes （830 pairs） potentially targeted by 79 significant microRNAs out of 204 using three cutoff criteria, i.e.,false discovery rate （FDR） 〈 0.1, opposite correlation coefficient between the tumor and normal samples, and predicted by at least one of three target prediction databases. Therefore, MMiRNA- Tar provides researchers a convenient tool to visualize the co-relationship between microRNAs and mRNAs and to predict their targeting relationship. We believe that correlating expression profiles for microRNAs and mRNAs offers a complementary approach for elucidating their interactions.