Transcriptome structure variability in Saccharomyces cerevisiae strains determined with a newly developed assembly software

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Abstract

Background

RNA-seq studies have an important role for both large-scale analysis of gene expression and for transcriptome reconstruction. However, the lack of software specifically developed for the analysis of the transcriptome structure in lower eukaryotes, has so far limited the comparative studies among different species and strains.

Results

In order to fill this gap, an innovative software called ORA (Overlapped Reads Assembler) was developed. This software allows a simple and reliable analysis of the transcriptome structure in organisms with a low number of introns. It can also determine the size and the position of the untranslated regions (UTR) and of polycistronic transcripts. As a case study, we analyzed the transcriptional landscape of six S. cerevisiae strains in two different key steps of the fermentation process. This comparative analysis revealed differences in the UTR regions of transcripts. By extending the transcriptome analysis to yeast species belonging to the Saccharomyces genus, it was possible to examine the conservation level of unknown non-coding RNAs and their putative functional role.

Conclusions

By comparing the results obtained using ORA with previous studies and with the transcriptome structure determined with other software, it was proven that ORA has a remarkable reliability. The results obtained from the training set made it possible to detect the presence of transcripts with variable UTRs between S. cerevisiae strains. Finally, we propose a regulatory role for some non-coding transcripts conserved within the Saccharomyces genus and localized in the antisense strand to genes involved in meiosis and cell wall biosynthesis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1045) contains supplementary material, which is available to authorized users.

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Most cited references 48

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The transcriptional landscape of the yeast genome defined by RNA sequencing.

U Nagalakshmi, Z. Wang, K. Waern … (2008)

The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.

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Sequencing and comparison of yeast species to identify genes and regulatory elements.

Manolis Kellis, Nick Patterson, Matthew G. Endrizzi … (2003)

Identifying the functional elements encoded in a genome is one of the principal challenges in modern biology. Comparative genomics should offer a powerful, general approach. Here, we present a comparative analysis of the yeast Saccharomyces cerevisiae based on high-quality draft sequences of three related species (S. paradoxus, S. mikatae and S. bayanus). We first aligned the genomes and characterized their evolution, defining the regions and mechanisms of change. We then developed methods for direct identification of genes and regulatory motifs. The gene analysis yielded a major revision to the yeast gene catalogue, affecting approximately 15% of all genes and reducing the total count by about 500 genes. The motif analysis automatically identified 72 genome-wide elements, including most known regulatory motifs and numerous new motifs. We inferred a putative function for most of these motifs, and provided insights into their combinatorial interactions. The results have implications for genome analysis of diverse organisms, including the human.

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Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs

Mitchell Guttman, Manuel Garber, Joshua Levin … (2010)

RNA-Seq provides an unbiased way to study a transcriptome, including both coding and non-coding genes. To date, most RNA-Seq studies have critically depended on existing annotations, and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We apply it to mouse embryonic stem cells, neuronal precursor cells, and lung fibroblasts to accurately reconstruct the full-length gene structures for the vast majority of known expressed genes. We identify substantial variation in protein-coding genes, including thousands of novel 5′-start sites, 3′-ends, and internal coding exons. We then determine the gene structures of over a thousand lincRNA and antisense loci. Our results open the way to direct experimental manipulation of thousands of non-coding RNAs, and demonstrate the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes.

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Author and article information

Contributors

Alessandro Sardu: Alessandro.sardu@unifr.ch

Laura Treu: latr@env.dtu.dk

Stefano Campanaro: stefano.campanaro@unipd.it

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 1 December 2014

Publication date Collection: 2014

Volume: 15

Issue: 1

Electronic Location Identifier: 1045

Affiliations

[ ]Department of Biology, Division of Biochemistry, University of Fribourg, CH-1700 Fribourg, Switzerland

[ ]Department of Environmental Engineering, Technical University of Denmark, DK-2800 Kgs Lyngby, Denmark

[ ]Department of Biology, University of Padova, Via Ugo Bassi 58/b, 35131 Padova, Italy

Article

Publisher ID: 6763

DOI: 10.1186/1471-2164-15-1045

PMC ID: 4302112

PubMed ID: 25441755

SO-VID: ec77fd6b-345c-40e9-a28c-488698906c47

License:

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 18 September 2014

Date accepted : 20 November 2014

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: saccharomyces cerevisiae,saccharomyces sensu-stricto,transcriptome assembly software,transcriptome variability,utr,non-coding rna,cell wall,reproductive process in single-celled organism

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ScienceOpen disciplines: Genetics

Keywords: saccharomyces cerevisiae, saccharomyces sensu-stricto, transcriptome assembly software, transcriptome variability, utr, non-coding rna, cell wall, reproductive process in single-celled organism

Transcriptome structure variability in Saccharomyces cerevisiae strains determined with a newly developed assembly software

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Abstract

Background

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Genomic Prediction

Most cited references 48

The transcriptional landscape of the yeast genome defined by RNA sequencing.

Sequencing and comparison of yeast species to identify genes and regulatory elements.

Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs

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