title: Combining transcriptome assemblies from multiple de novo assemblers in the allo-tetraploid plant Nicotiana benthamiana.
abstract: BACKGROUND
  Nicotiana 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  To 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.
authors.affiliation: School of Biological Sciences, University of Sydney, Sydney, Australia.
authors.forename: Kenlee
authors.initials: K
authors.lastname: Nakasugi
authors.affiliation: Mount Albert Research Centre, Plant & Food Research, Auckland, New Zealand.
authors.forename: Ross
authors.initials: R
authors.lastname: Crowhurst
authors.affiliation: School of Biological Sciences, University of Sydney, Sydney, Australia.
authors.forename: Julia
authors.initials: J
authors.lastname: Bally
authors.affiliation: School of Biological Sciences, University of Sydney, Sydney, Australia; The Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, Australia.
authors.forename: Peter
authors.initials: P
authors.lastname: Waterhouse
mesh_terms: D016000:Cluster Analysis; D030562:Databases, Protein; D059014:High-Throughput Nucleotide Sequencing / Q000295:instrumentation* / Q000379:methods*; D058977:Molecular Sequence Annotation; D010940:Plant Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D034622:RNA Interference; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D057891:Tetraploidy; D014026:Nicotiana / Q000235:genetics*; D059467:Transcriptome / Q000235:genetics*
chemical_list: D010940:Plant Proteins; D012333:RNA, Messenger