Characterization of a de novo assembled transcriptome of the Common Blackbird (Turdus merula)

转录组 从头转录组组装 生物 基因组 参考基因组 顺序装配 计算生物学 遗传学 RNA序列 基因组学 基因 基因表达
作者
Sven Koglin,Daronja Trense,Michaël Wink,Hedwig Sauer‐Gürth,Dieter Thomas Tietze
出处
期刊:PeerJ [PeerJ, Inc.]
卷期号:5: e4045-e4045 被引量:4
标识
DOI:10.7717/peerj.4045
摘要

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: 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: 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.

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