Objective

Brown trout (Salmo trutta) has been extensively studied by its commercial and biological importance. From the sixty-six species in this family, S. trutta is a species native to Europe with a wide distribution area that includes Atlantic and Mediterranean European basins, as well as northern Africa and western Asia basins [1, 2].The specie has been introduced in North and South America and Australia by its commercial exploitation for sport fishing, as well as farmed for food and game fish, extending their actual geographical distribution as discontinuous populations on all continents except Antarctica [3].

Life history traits of Brown trout populations include resident forms such as riverine (S. trutta m. fario) and migratory forms such as anadromous (S. trutta m. trutta) ecotype [4, 5]. Anadromous and non-anadromous forms coexist in the same river being apparently genetically indistinguishable [6, 7]. An extended literature on Brown trout research has been produced that includes physiological, ecological and genetic aspects [8,9,10]. As a contribution to this global effort, here we provide a comprehensive transcriptome data set derived from brain and muscle tissues of Salmo trutta m. fario ecotype by using RNA-seq technology. We also evaluated differential transcript expression among these two tissues identifying key differentially expressed unigenes. Finally, we applied an in-silico pipeline that allow us to discover SNP variants useful for population genomic studies. The generated data could provide new valuable genomic resources for population genetic and genomic studies that can help to answer opened questions about the live history traits of riverine S. trutta m. fario as well as differences among S. trutta ecotypes.

Data description

Salmo trutta m. fario. brain and muscle tissues were collected from 25 wild type individuals (15 females) captured at the Falmisell river (Lleida, Catalonia). RNA pools from brain (10.2 µg) and muscle (11.4 µg) tissues were obtained with equimolar concentration from each subject. The TruSeq™ RNA sample Prep Kit (Illumina, Madrid, Spain) was used to build cDNA libraries according to manufacturer instructions (Table 1, Data file 1). FASTQ sequence reads were assembled using Trinity [11] run on the paired end sequences with the fixed default k-mer size of 25 and minimum contig length of 200. Descriptive statistics of assembly and sequencing is found at Table 1 (Data file 2 and Data file 3). Among the 144,984 contigs predicted by Trinity (Table 1, Data file 4 and Data file 8), we identify protein coding regions using TransDecoder package [11]. We retained the longest ORF predicted for each contig sequence with a minimum of 100 amino acids long. Transcript redundancy was further reduced by CD-hit [12], obtaining a final set of 35,189 non-redundant ORF unigenes as best cluster representatives (Table 1, Data file 5). Size distribution for clustered ORF unigenes is presented in Table 1 (Data file 3). This final set was characterized by homology search to nucleotide and protein databases (Table 1, Data file 10 and Data file 11). Taxonomic representation showed the top hits for a large fraction of unigenes (≈88%) to Neopterigii taxon, with 66% of unigenes assigned to family Salmonidae (Salvelius sp. (1%), Onchorrinchus sp. (14%) and Salmo sp. (51%) (Table 1, Data file 12). A total of 4337 protein motif were assigned to 23,616 ORF unigenes, being the RNA recognition motif (6.4%), Immunoglobulin domain (4.8%), Tetratricopeptide repeat (4.8%) and Protein kinase domain (3.4%) the most prevalent (Table 1, Data file 13).

Table 1 Overview of data files/data sets
Full size table

Similarity search by Blast2GO renders a total of 28,132 (80%) unigenes with GO annotation. GO term were then simplified using a generic GOSlim vocabulary [13] (Table 1, Data file 14). The ten top GO terms among the Cellular Component (18,071, 64%), Molecular Function (20,691, 74%) and Biological Process (23,954, 85%) ontology at level 2 are shown in Table 1 (Data file 4). Mapping unigenes to the reference canonical pathways in the KEGG database, yields a total of 13,957 (39.8%) ORF unigenes assigned to 3421 KEGG terms (KO) defining a total of 386 pathways (Table 1, Data file 15).

Tissue specific transcriptome expression analysis was performed by normalization of raw reads (FPKM, fragments per kilobase of exon per million fragments) obtained from both tissues (Table 1, Data file 16 and Data file 17). Analysis reveals 1172 ORF unigenes expressed only in muscle, 8595 expressed only in brain and 12,072 expressed in both tissues (Table 1, Data file 5, FigS3). Differentially expressed unigenes at FDR < 0.01 and best homologous sequences are shown at Table 1 (Data file 18 and Data file 19).

Finally, we have identified 73,237 putative SNPs (Table 1, Data file 20) and extracted 150 bp sequence context to each SNP as a source for the design of PCR primers useful for genotyping protocols (Table 1, Data file 21).

Limitations

The use of pooled RNA samples does not allow us to detect sex or individual specific transcript expression profiles as well as limit our capability to detect transcripts expressed at low level in a specific individual. In addition, pooled samples avoid us to resolve SNP frequency distribution, being this parameter indirectly estimated according to the observed SNP sequence coverage in the pooled sample.