RNA-seq

Transcript analysis of a goat mesenteric lymph node by deep next-generation sequencing

G. X. E, Zhao, Y. J., Na, R. S., Huang, Y. F., E, G. X., Zhao, Y. J., Na, R. S., and Huang, Y. F., Transcript analysis of a goat mesenteric lymph node by deep next-generation sequencing, vol. 15, p. -, 2016.

Deep RNA sequencing (RNA-seq) provides a practical and inexpensive alternative for exploring genomic data in non-model organisms. The functional annotation of non-model mammalian genomes, such as that of goats, is still poor compared to that of humans and mice. In the current study, we performed a whole transcriptome analysis of an intestinal mucous membrane lymph node to comprehensively characterize the transcript catalogue of this tissue in a goat. Using an Illumina HiSeq 4000 sequencing platform, 9.692 GB of raw reads were acquired.

Comparative analysis of the liver tissue transcriptomes of Mongolian and Lanzhou fat-tailed sheep

X. Cheng, Zhao, S. G., Yue, Y., Liu, Z., Li, H. W., Wu, J. P., Cheng, X., Zhao, S. G., Yue, Y., Liu, Z., Li, H. W., and Wu, J. P., Comparative analysis of the liver tissue transcriptomes of Mongolian and Lanzhou fat-tailed sheep, vol. 15, p. -, 2016.

Research on gene regulation has been made possible with the help of RNA sequencing applications such as RNA-Seq technology for high-throughput sequencing platforms. Recent studies have explored the transcriptomes from different tissues of domestic animals using RNA-Seq technology, but little research has been done to study the transcriptomes of breeds of sheep having different adipose tissue deposition mechanisms, such as Mongolian and Lanzhou fat-tailed sheep.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data

L. Zhang, Liu, X. J., Zhang, L., and Liu, X. J., A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data, vol. 15, p. -, 2016.

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data.

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