Investigation of chicken housekeeping genes using next-generation sequencing data

Accurate normalization of the gene expression assays, using housekeeping genes (HKGs), is critically necessary. To do so, selection of a proper set of HKGs for a specific experiment is of great importance. Despite many studies, there is no consensus about the suitable set of HKGs for implementing in the quantitative real-time PCR analyses of chicken tissues. A limited number of HKGs have been widely used. However, wide utilization of a little number of HKGs for all tissues is challenging. The emergence of high-throughput gene expression RNA-seq data has enabled the simultaneous comparison of the stability of multiple HKGs. Therefore, employing the average coefficient of variations of at least three datasets per tissue, we sorted all reliably expressed genes (REGs; with FPKM ≥ 1 in at least one sample) and introduced the top 10 most suitable and stable reference genes for each of the 16 chicken tissues. We evaluated the consistency of the results of five tissues using the same methodology on other datasets. Furthermore, we assessed 96 previously widely used HKGs (WU-HKGs) in order to challenge the accuracy of the previous studies. The New Tuxedo software suite was used for the main analyses. The results revealed novel, different sets of reference genes for each of the tissues with 17 common genes among the top 10 genes lists of 16 tissues. The results did disprove the suitability of WU-HKGs such as Actb, Ldha, Scd, B2m, and Hprt1 for any of the tissues examined. On the contrary, a total of 6, 13, 14, 23, and 32 validated housekeeping genes (V-HKGs) were discovered as the most stable and suitable reference genes for muscle, spleen, liver, heart, and kidney tissues, respectively. Although we identified a few new HKGs usable for multiple tissues, the selection of suitable HKGs is required to be tissue specific. The newly introduced reference genes from the present study, despite lacking experimental validation, will be able to contribute to the more accurate normalization for future expression analysis of chicken genes.