Species living in a changing environment are capable of adapting to alterations of various factors. Physiological acclimatization may be significantly influenced by the heterozygosity, especially with regards to allele variance and its specific expression (ASE) under different conditions. Data from RNA-seq experiments can be used to identify and quantify the alleles expressed, in order to detect and characterize ASE and regulation of gene expression. However, the allele matching the reference genome creates a mapping bias that prevents a reliable estimation of the allele depth unless the haplotype of the experimental individuals is provided. We developed a pipeline that allows the identification of the alleles corresponding to an RNA-seq dataset and their unbiased quantification. This pipeline does not require the sequencing of the DNA nor the previous knowledge of the haplotype. The identified SNPs are further substituted in the reference genome, thus creating two pseudogenomes with the alternative alleles on two independent samples of the experiment. The SNPs are further called against each pseudogenome thus providing with two SNP datasets that are averaged for calculation of the allele depth. The final SNP calling file contains the coordinates of the SNPs and also the ID of genes containing the SNPs, the expressed genotypes, the unbiased allele depth and the statistical tests for identifying ASE according to the experimental design and correlated with differentially expressed genes. Therefore, the pipeline presented here can calculate ASE in non-model organisms and can be applied to previous RNA-seq datasets for expanding studies in gene expression regulation.

Interactions of organisms with their environment are complex and environmental regulation at different levels of biological organization is often non-linear. Therefore, the genotype to phenotype continuum requires study at multiple levels of organization. While studies of transcriptome regulation are now common for many species, quantitative studies of environmental effects on proteomes are needed. Here we report the generation of a data-independent acquisition (DIA) assay library that enables simultaneous targeted proteomics of thousands of Oreochromis niloticus kidney proteins using a label- and gel-free workflow that is well suited for ecologically relevant field samples. We demonstrate the usefulness of this DIA assay library by discerning environmental effects on the kidney proteome of O. niloticus. Moreover, we demonstrate that the DIA assay library approach generates data that are complimentary rather than redundant to transcriptomics data. Transcript and protein abundance differences in kidneys of tilapia acclimated to freshwater and brackish water (25 g/kg) were correlated for 2114 unique genes. A high degree of non-linearity in salinity-dependent regulation of transcriptomes and proteomes was revealed suggesting that the regulation of O. niloticus renal function by environmental salinity relies heavily on post-transcriptional mechanisms. The application of functional enrichment analyses using STRING and KEGG to DIA assay datasets is demonstrated by identifying myo-inositol metabolism, antioxidant and xenobiotic functions, and signaling mechanisms as key elements controlled by salinity in tilapia kidneys. The DIA assay library resource presented here can be adopted for other tissues and other organisms to study proteome dynamics during changing ecological contexts.