Background: Genome research in farm animals will expand our basic knowledge of the genetic control of complex traits, and the results will be applied in the livestock industry to improve meat quality and productivity, as well as to reduce the incidence of disease. A combination of quantitative trait locus mapping and microarray analysis is a useful approach to reduce the overall effort needed to identify genes associated with quantitative traits of interest.
Results: We constructed a full-length enriched cDNA library from porcine backfat tissue. The estimated average size of the cDNA inserts was 1.7 kb, and the cDNA fullness ratio was 70%. In total, we deposited 16,110 high-quality sequences in the dbEST division of GenBank (accession numbers: DT319652-DT335761). For all the expressed sequence tags (ESTs), approximately 10.9 Mb of porcine sequence were generated with an average length of 674 bp per EST (range: 200-952 bp). Clustering and assembly of these ESTs resulted in a total of 5,008 unique sequences with 1,776 contigs (35.46%) and 3,232 singleton (65.54%) ESTs. From a total of 5,008 unique sequences, 3,154 (62.98%) were similar to other sequences, and 1,854 (37.02%) were identified as having no hit or low identity (<95%) and 60% coverage in The Institute for Genomic Research (TIGR) gene index of Sus scrofa. Gene ontology (GO) annotation of unique sequences showed that approximately 31.7, 32.3, and 30.8% were assigned molecular function, biological process, and cellular component GO terms, respectively. A total of 1,854 putative novel transcripts resulted after comparison and filtering with the TIGR SsGI; these included a large percentage of singletons (80.64%) and a small proportion of contigs (13.36%).
Conclusion: The sequence data generated in this study will provide valuable information for studying expression profiles using EST-based microarrays and assist in the condensation of current pig TCs into clusters representing longer stretches of cDNA sequences. The isolation of genes expressed in backfat tissue is the first step toward a better understanding of backfat tissue on a genomic basis.