ARS | Publication request: De novo Reconstruction of the Pig Skeletal Muscle Transcriptome for Identification of MicroRNA Gene Targets

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: January 20, 2010
Publication Date: February 24, 2010
Citation: McDaneld, T.G., Wiedmann, R.T., Harhay, G.P., Smith, T.P.L. 2010. De novo Reconstruction of the Pig Skeletal Muscle Transcriptome for Identification of MicroRNA Gene Targets (abstract). In: Proceedings of the Advances in Genome Biology & Technology Conference, February 24-27,2010, Marco Island, Florida. p. 114.

Technical Abstract: MicroRNA (miR) are a class of small RNAs that regulate gene expression by inhibiting translation of protein encoding transcripts. Inhibition is exerted through targeting of a miR-protein complex by base-pairing of the miR sequence to cognate recognition sequences in the mRNA. Target identification for a given miR sequence is generally accomplished by informatics analysis of predicted mRNA sequences present in the genome or in databases of transcript sequence for the tissue of interest. However, gene models for swine muscle transcripts in current databases are inadequate for this exercise. To provide the data necessary to identify gene targets for miR in skeletal muscle of swine, normalized cDNA libraries were sequenced using Roche GS-FLX pyrosequencing and a de novo assembly of transcripts was performed using the MIRA sequence assembly program. Over 408 million bases of sequence were generated, which assembled into approximately 72,000 contigs for analysis, providing data on multiple splice forms for some genes and detecting rare transcript forms. Contigs from this analysis were compared to sequences in GenBank to identify contigs containing complete CDS and determine putative protein products. The 3’ UTR portions of these transcripts, which commonly contain the seed sequence for microRNA binding, were then examined to identify targets for miR that had been separately sequenced from the same swine muscle samples. MicroRNA binding sites were then computationally identified. Our results demonstrate that 454 sequencing is an efficient method to generate sequence for assembly of eukaryote transcriptomes that can be utilized in various applications including identification of microRNA gene targets.