Characterization of a assembled transcriptome of the Common Blackbird ().

BACKGROUND

In recent years, next generation high throughput sequencing technologies have proven to be useful tools for investigations concerning the genomics or transcriptomics also of non-model species. Consequently, ornithologists have adopted these technologies and the respective bioinformatics tools to survey the genomes and transcriptomes of a few avian non-model species. The Common Blackbird is one of the most common bird species living in European cities, which has successfully colonized urban areas and for which no reference genome or transcriptome is publicly available. However, to target questions like genome wide gene expression analysis, a reference genome or transcriptome is needed.

METHODS

Therefore, in this study two Common Blackbirds were sacrificed, their mRNA was isolated and analyzed by RNA-Seq to assemble a transcriptome and characterize it. Illumina reads (125 bp paired-end) and a Velvet/Oases pipeline led to 162,158 transcripts. For the annotation (using Blast+), an unfiltered protein database was used. SNPs were identified using SAMtools and BCFtools. Furthermore, mRNA from three single tissues (brain, heart and liver) of the same two Common Blackbirds were sequenced by Illumina (75 bp single-end reads). The draft transcriptome and the three single tissues were compared by their BLAST hits with the package VennDiagram in R.

RESULTS

Following the annotation against protein databases, we found evidence for 15,580 genes in the transcriptome (all well characterized hits after annotation). On 18% of the assembled transcripts, 144,742 SNPs were identified which are, consequently, 0.09% of all nucleotides in the assembled transcriptome. In the transcriptome and in the single tissues (brain, heart and liver), 10,182 shared genes were found.

DISCUSSION

Using a next-generation technology and bioinformatics tools, we made a first step towards the genomic investigation of the Common Blackbird. The assembled transcriptome is usable for downstream analyses such as differential gene expression analysis and SNP identification. This study shows the importance of the approach to sequence single tissues to understand functions of tissues, proteins and the phenotype.