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Genes, Volume 7, Issue 3 (March 2016) – 2 articles

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4056 KiB

Open AccessArticle

Genome-Wide Identification and Characterization of Long Non-Coding RNAs from Mulberry (Morus notabilis) RNA-seq Data

by Xiaobo Song, Liang Sun, Haitao Luo, Qingguo Ma, Yi Zhao and Dong Pei

Genes 2016, 7(3), 11; https://doi.org/10.3390/genes7030011 - 29 Feb 2016

Cited by 39 | Viewed by 7365

Abstract

Numerous sources of evidence suggest that most of the eukaryotic genome is transcribed into protein-coding mRNAs and also into a large number of non-coding RNAs (ncRNAs). Long ncRNAs (lncRNAs), a group consisting of ncRNAs longer than 200 nucleotides, have been found to play critical roles in transcriptional, post-transcriptional, and epigenetic gene regulation across all kingdoms of life. However, lncRNAs and their regulatory roles remain poorly characterized in plants, especially in woody plants. In this paper, we used a computational approach to identify novel lncRNAs from a published RNA-seq data set and analyzed their sequences and expression patterns. In total, 1133 novel lncRNAs were identified in mulberry, and 106 of these lncRNAs displayed a predominant tissue-specific expression in the five major tissues investigated. Additionally, functional predictions revealed that tissue-specific lncRNAs adjacent to protein-coding genes might play important regulatory roles in the development of floral organ and root in mulberry. The pipeline used in this study would be useful for the identification of lncRNAs obtained from other deep sequencing data. Furthermore, the predicted lncRNAs would be beneficial towards an understanding of the variations in gene expression in plants. Full article

(This article belongs to the Section Molecular Genetics and Genomics)

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2552 KiB

Open AccessArticle

Pseudo-Reference-Based Assembly of Vertebrate Transcriptomes

by Kyoungwoo Nam, Heesu Jeong and Jin-Wu Nam

Genes 2016, 7(3), 10; https://doi.org/10.3390/genes7030010 - 24 Feb 2016

Cited by 1 | Viewed by 6542

Abstract

High-throughput RNA sequencing (RNA-seq) provides a comprehensive picture of the transcriptome, including the identity, structure, quantity, and variability of expressed transcripts in cells, through the assembly of sequenced short RNA-seq reads. Although the reference-based approach guarantees the high quality of the resulting transcriptome, this approach is only applicable when the relevant reference genome is present. Here, we developed a pseudo-reference-based assembly (PRA) that reconstructs a transcriptome based on a linear regression function of the optimized mapping parameters and genetic distances of the closest species. Using the linear model, we reconstructed transcriptomes of four different aves, the white leg horn, turkey, duck, and zebra finch, with the Gallus gallus genome as a pseudo-reference, and of three primates, the chimpanzee, gorilla, and macaque, with the human genome as a pseudo-reference. The resulting transcriptomes show that the PRAs outperformed the de novo approach for species with within about 10% mutation rate among orthologous transcriptomes, enough to cover distantly related species as far as chicken and duck. Taken together, we suggest that the PRA method can be used as a tool for reconstructing transcriptome maps of vertebrates whose genomes have not yet been sequenced. Full article

(This article belongs to the Section Technologies and Resources for Genetics)

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Genes, Volume 7, Issue 3 (March 2016) – 2 articles

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