Characterization of transcriptional complexity during longissimus muscle development in bovines using high-throughput sequencing

Abstract

Background: Beef cattle are among the most economically important animals in the world because they are farmed for their meat and leather. However, a lack of genetic information remains an obstacle to understanding the mechanisms behind the development of this animal. Analysis of the beef cattle transcriptome and its expression profile data are essential to extending the genetic information resources for this species and would support studies on this animal.

Results: RNA sequencing of beef cattle was performed using the Illumina High-Seq2000 platform. A total of 25,605,140 and 26,214,800 reads were sequenced for embryonic and adult pooled samples, respectively. We identified 24,464-29,994 novel transcript units in two pooled samples. In addition, 8,533-10,144 genes showed evidence of alternative splicing, in agreement with the finding that alternative 3' splicing is the most common type of alternative splicing event in cattle. We detected the expression levels of 16,174 genes, and 6,800 genes exhibited differential expression between the two pooled samples with a false discovery rate ≤0.001. Using GO enrichment and KEGG pathway analysis, multiple GO term and biological pathways were found to be significantly enriched for differentially expressed genes. In addition, we discovered that 30,618-31,334 putative single nucleotide polymorphisms were located in coding regions.

Conclusions: We obtained a high-quality beef cattle reference transcriptome using a high throughput sequencing approach, thereby providing a valuable resource for better understanding the beef cattle genome. The transcriptome data will facilitate future functional studies on the beef cattle genome and can be applied to breeding programs for cattle and closely related mammals.

Figure 1. Statistics of novel transcripts.

The abscissa shows the samples 30M and Emb135, and the ordinate represents the number of novel transcripts occurred in each sample.

Figure 2. Statistics of alternative splicing events and genes in two samples.

(A): The red bars show the distribution of the number of genes for each type of alternative splicing mode in the pooled 30M sample. The green bars indicate the number of alternative splicing events that occurred in each gene. (B) is same as A except that the results for the Emb135 sample are presented.

Figure 3. Statistics of differentially expressed genes (DEGs).

Red and green bars above the X-axis denote genes with up- and down-regulated expression in Emb135-VS-30M samples, respectively. The Y-axis denote number of differentially expressed genes with up- and down-regulated expression in Emb135-VS-30M samples, respectively.

Figure 4. The top 20 Gene Ontology functional annotations for the differentially expressed genes.

Bar graphs (A), (B) and (C) show three independent Gene Ontology (GO) information categories: cellular components, molecular functions and biological processes, respectively. In each bar graph, the abscissa represents the number of differentially expressed genes, and the ordinate is the ID number of the GO terms. All GO categories listed have enrichment P values <0.05.

Figure 5. Pipeline of SNP Calling.

The SNPs can be identified on the consensus sequence through the comparison with the reference.