Dataset Information

TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs.

ABSTRACT: Transcriptome assemblers aim to reconstruct full-length transcripts from RNA-seq data. We present TransComb, a genome-guided assembler developed based on a junction graph, weighted by a bin-packing strategy and paired-end information. A newly designed extension method based on weighted junction graphs can accurately extract paths representing expressed transcripts, whether they have low or high expression levels. Tested on both simulated and real datasets, TransComb demonstrates significant improvements in both recall and precision over leading assemblers, including StringTie, Cufflinks, Bayesembler, and Traph. In addition, it runs much faster and requires less memory on average. TransComb is available at http://sourceforge.net/projects/transcriptomeassembly/files/ .

SUBMITTER: Liu J

PROVIDER: S-EPMC5069867 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs.

Liu Juntao J Yu Ting T Jiang Tao T Li Guojun G

Genome biology 20161019 1

Transcriptome assemblers aim to reconstruct full-length transcripts from RNA-seq data. We present TransComb, a genome-guided assembler developed based on a junction graph, weighted by a bin-packing strategy and paired-end information. A newly designed extension method based on weighted junction graphs can accurately extract paths representing expressed transcripts, whether they have low or high expression levels. Tested on both simulated and real datasets, TransComb demonstrates significant impr ...[more]

PMID: 27760567

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Project description:Pines are the most important tree species to the international forestry industry, covering 42 % of the global industrial forest plantation area. One of the most pressing threats to cultivation of some pine species is the pitch canker fungus, Fusarium circinatum, which can have devastating effects in both the field and nursery. Investigation of the Pinus-F. circinatum host-pathogen interaction is crucial for development of effective disease management strategies. As with many non-model organisms, investigation of host-pathogen interactions in pine species is hampered by limited genomic resources. This was partially alleviated through release of the 22 Gbp Pinus taeda v1.01 genome sequence ( http://pinegenome.org/pinerefseq/ ) in 2014. Despite the fact that the fragmented state of the genome may hamper comprehensive transcriptome analysis, it is possible to leverage the inherent redundancy resulting from deep RNA sequencing with Illumina short reads to assemble transcripts in the absence of a completed reference sequence. These data can then be integrated with available genomic data to produce a comprehensive transcriptome resource. The aim of this study was to provide a foundation for gene expression analysis of disease response mechanisms in Pinus patula through transcriptome assembly.Eighteen de novo and two reference based assemblies were produced for P. patula shoot tissue. For this purpose three transcriptome assemblers, Trinity, Velvet/OASES and SOAPdenovo-Trans, were used to maximise diversity and completeness of assembled transcripts. Redundancy in the assembly was reduced using the EvidentialGene pipeline. The resulting 52 Mb P. patula v1.0 shoot transcriptome consists of 52 112 unigenes, 60 % of which could be functionally annotated.The assembled transcriptome will serve as a major genomic resource for future investigation of P. patula and represents the largest gene catalogue produced to date for this species. Furthermore, this assembly can help detect gene-based genetic markers for P. patula and the comparative assembly workflow could be applied to generate similar resources for other non-model species.

Dataset Information

TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs.

Publications

TransComb: genome-guided transcriptome assembly via combing junctions in splicing graphs.

Similar Datasets

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