Dataset Information

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.

ABSTRACT:

Background

Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied.

Results

Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95%) and reconstruct full-length genes for the majority of the existing gene models (54.3%). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics.

Conclusions

These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome.

SUBMITTER: Martin J

PROVIDER: S-EPMC3152782 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.

Martin Jeffrey J Bruno Vincent M VM Fang Zhide Z Meng Xiandong X Blow Matthew M Zhang Tao T Sherlock Gavin G Snyder Michael M Wang Zhong Z

BMC genomics 20101124

<h4>Background</h4>Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied.<h4>Results</h4>Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de ...[more]

PMID: 21106091

Similar Datasets

Project description:BACKGROUND:The main challenge in de novo genome assembly of DNA-seq data is certainly to deal with repeats that are longer than the reads. In de novo transcriptome assembly of RNA-seq reads, on the other hand, this problem has been underestimated so far. Even though we have fewer and shorter repeated sequences in transcriptomics, they do create ambiguities and confuse assemblers if not addressed properly. Most transcriptome assemblers of short reads are based on de Bruijn graphs (DBG) and have no clear and explicit model for repeats in RNA-seq data, relying instead on heuristics to deal with them. RESULTS:The results of this work are threefold. First, we introduce a formal model for representing high copy-number and low-divergence repeats in RNA-seq data and exploit its properties to infer a combinatorial characteristic of repeat-associated subgraphs. We show that the problem of identifying such subgraphs in a DBG is NP-complete. Second, we show that in the specific case of local assembly of alternative splicing (AS) events, we can implicitly avoid such subgraphs, and we present an efficient algorithm to enumerate AS events that are not included in repeats. Using simulated data, we show that this strategy is significantly more sensitive and precise than the previous version of KisSplice (Sacomoto et al. in WABI, pp 99-111, 1), Trinity (Grabherr et al. in Nat Biotechnol 29(7):644-652, 2), and Oases (Schulz et al. in Bioinformatics 28(8):1086-1092, 3), for the specific task of calling AS events. Third, we turn our focus to full-length transcriptome assembly, and we show that exploring the topology of DBGs can improve de novo transcriptome evaluation methods. Based on the observation that repeats create complicated regions in a DBG, and when assemblers try to traverse these regions, they can infer erroneous transcripts, we propose a measure to flag transcripts traversing such troublesome regions, thereby giving a confidence level for each transcript. The originality of our work when compared to other transcriptome evaluation methods is that we use only the topology of the DBG, and not read nor coverage information. We show that our simple method gives better results than Rsem-Eval (Li et al. in Genome Biol 15(12):553, 4) and TransRate (Smith-Unna et al. in Genome Res 26(8):1134-1144, 5) on both real and simulated datasets for detecting chimeras, and therefore is able to capture assembly errors missed by these methods.

Project description:BackgroundField pea (Pisum sativum L.) is a cool-season grain legume that is cultivated world-wide for both human consumption and stock-feed purposes. Enhancement of genetic and genomic resources for field pea will permit improved understanding of the control of traits relevant to crop productivity and quality. Advances in second-generation sequencing and associated bioinformatics analysis now provide unprecedented opportunities for the development of such resources. The objective of this study was to perform transcriptome sequencing and characterisation from two genotypes of field pea that differ in terms of seed and plant morphological characteristics.ResultsTranscriptome sequencing was performed with RNA templates from multiple tissues of the field pea genotypes Kaspa and Parafield. Tissue samples were collected at various growth stages, and a total of 23 cDNA libraries were sequenced using Illumina high-throughput sequencing platforms. A total of 407 and 352 million paired-end reads from the Kaspa and Parafield transcriptomes, respectively were assembled into 129,282 and 149,272 contigs, which were filtered on the basis of known gene annotations, presence of open reading frames (ORFs), reciprocal matches and degree of coverage. Totals of 126,335 contigs from Kaspa and 145,730 from Parafield were subsequently selected as the reference set. Reciprocal sequence analysis revealed that c. 87% of contigs were expressed in both cultivars, while a small proportion were unique to each genotype. Reads from different libraries were aligned to the genotype-specific assemblies in order to identify and characterise expression of contigs on a tissue-specific basis, of which 87% were expressed in more than one tissue, while others showed distinct expression patterns in specific tissues, providing unique transcriptome signatures.ConclusionThis study provided a comprehensive assembled and annotated transcriptome set for field pea that can be used for development of genetic markers, in order to assess genetic diversity, construct linkage maps, perform trait-dissection and implement whole-genome selection strategies in varietal improvement programs, as well to identify target genes for genetic modification approaches on the basis of annotation and expression analysis. In addition, the reference field pea transcriptome will prove highly valuable for comparative genomics studies and construction of a finalised genome sequence.

Dataset Information

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.

Background

Results

Conclusions

Publications

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets