Evidence map›Paper›PMID 24614631›Full record

ArticlePloS one2014

Combining transcriptome assemblies from multiple de novo assemblers in the allo-tetraploid plant Nicotiana benthamiana.

Kenlee Nakasugi, Ross Crowhurst, Julia Bally, Peter Waterhouse

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

88 citing papers in PubMed.

  1. MolluscaGenes: A Transcriptomic Database for the Mollusca.bioRxiv : the preprint server for biology · 2026
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  14. Elucidating the Mesocarp Drupe Transcriptome of Açai (International journal of molecular sciences · 2023
    Article
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28 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kenlee NakasugiSchool of Biological Sciences, University of Sydney, Sydney, Australia.
Ross CrowhurstMount Albert Research Centre, Plant & Food Research, Auckland, New Zealand.
Julia BallySchool of Biological Sciences, University of Sydney, Sydney, Australia.
Peter WaterhouseSchool of Biological Sciences, University of Sydney, Sydney, Australia; The Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNicotiana benthamiana is an allo-tetraploid plant, which can be challenging for de novo transcriptome assemblies due to homeologous and duplicated gene copies. Transcripts generated from such genes can be distinct yet highly similar in sequence, with markedly differing expression levels. This can lead to unassembled, partially assembled or mis-assembled contigs. Due to the different properties of de novo assemblers, no one assembler with any one given parameter space can re-assemble all possible transcripts from a transcriptome.

resultsIn an effort to maximise the diversity and completeness of de novo assembled transcripts, we utilised four de novo transcriptome assemblers, TransAbyss, Trinity, SOAPdenovo-Trans, and Oases, using a range of k-mer sizes and different input RNA-seq read counts. We complemented the parameter space biologically by using RNA from 10 plant tissues. We then combined the output of all assemblies into a large super-set of sequences. Using a method from the EvidentialGene pipeline, the combined assembly was reduced from 9.9 million de novo assembled transcripts to about 235,000 of which about 50,000 were classified as primary. Metrics such as average bit-scores, feature response curves and the ability to distinguish paralogous or homeologous transcripts, indicated that the EvidentialGene processed assembly was of high quality. Of 35 RNA silencing gene transcripts, 34 were identified as assembled to full length, whereas in a previous assembly using only one assembler, 9 of these were partially assembled.

conclusionsTo achieve a high quality transcriptome, it is advantageous to implement and combine the output from as many different de novo assemblers as possible. We have in essence taking the 'best' output from each assembler while minimising sequence redundancy. We have also shown that simultaneous assessment of a variety of metrics, not just focused on contig length, is necessary to gauge the quality of assemblies.

Indexed as

TetraploidyCluster AnalysisDatabases, ProteinHigh-Throughput Nucleotide SequencingMolecular Sequence AnnotationNicotianaPlant ProteinsRNA InterferenceRNA, MessengerSequence Homology, Amino AcidTranscriptomePlant ProteinsRNA, Messenger

Identifiers

PMID24614631
PMCPMC3948916

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.