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De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris.

ABSTRACT: Orchids are renowned for their spectacular flowers and ecological adaptations. After the sequencing of the genome of the tropical epiphytic orchid Phalaenopsis equestris, we combined Illumina HiSeq2000 for RNA-Seq and Trinity for de novo assembly to characterize the transcriptomes for 11 diverse P. equestris tissues representing the root, stem, leaf, flower buds, column, lip, petal, sepal and three developmental stages of seeds. Our aims were to contribute to a better understanding of the molecular mechanisms driving the analysed tissue characteristics and to enrich the available data for P. equestris. Here, we present three databases. The first dataset is the RNA-Seq raw reads, which can be used to execute new experiments with different analysis approaches. The other two datasets allow different types of searches for candidate homologues. The second dataset includes the sets of assembled unigenes and predicted coding sequences and proteins, enabling a sequence-based search. The third dataset consists of the annotation results of the aligned unigenes versus the Nonredundant (Nr) protein database, Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) databases with low e-values, enabling a name-based search.

SUBMITTER: Niu SC

PROVIDER: S-EPMC5037975 | biostudies-literature |

REPOSITORIES: biostudies-literature

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De novo transcriptome assembly databases in the butterfly orchid Phalaenopsis equestris

Project description:Orchidaceae are renowned for their spectacular flowers as well as other reproductive and ecological adaptations. After the genome of the tropical epiphytic orchid Phalaenopsis equestris was sequenced, we combined Trinity data for de novo assembly and Illumina HiSeq1500 data for RNA-Seq analysis to characterize the transcriptomes of four different organs for a better understanding of the molecular mechanisms driving these characteristics. We present four de novo assembled transcripts reconstructed from RNA collected from the root, stem, leaf, and flower of Phalaenopsis equestris. These sets of transcripts greatly enrich the available data for Phalaenopsis equestris. Here, we present two databases, and each dataset allows for a different type of search for candidate homologues. The first dataset consists of the sets of assembled unigenes, which enable a sequence-based search. A comprehensive analysis of the assembled unigenes revealed the unigenes from root, stem, leaf, and flower with high e-values aligned versus the Nr, Swiss-Port, KEGG, COG, and GO database, respectively. This analysis enabled the production of a second database, which includes sequences correlated with annotated transcript names as well as the confidence of the best hit from BLAST.

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A de novo floral transcriptome reveals clues into Phalaenopsis orchid flower development.

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Transcriptome atlas of <i>Phalaenopsis equestris</i>.

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Informed kmer selection for de novo transcriptome assembly.

Project description:MotivationDe novo transcriptome assembly is an integral part for many RNA-seq workflows. Common applications include sequencing of non-model organisms, cancer or meta transcriptomes. Most de novo transcriptome assemblers use the de Bruijn graph (DBG) as the underlying data structure. The quality of the assemblies produced by such assemblers is highly influenced by the exact word length k As such no single kmer value leads to optimal results. Instead, DBGs over different kmer values are built and the assemblies are merged to improve sensitivity. However, no studies have investigated thoroughly the problem of automatically learning at which kmer value to stop the assembly. Instead a suboptimal selection of kmer values is often used in practice.ResultsHere we investigate the contribution of a single kmer value in a multi-kmer based assembly approach. We find that a comparative clustering of related assemblies can be used to estimate the importance of an additional kmer assembly. Using a model fit based algorithm we predict the kmer value at which no further assemblies are necessary. Our approach is tested with different de novo assemblers for datasets with different coverage values and read lengths. Further, we suggest a simple post processing step that significantly improves the quality of multi-kmer assemblies.ConclusionWe provide an automatic method for limiting the number of kmer values without a significant loss in assembly quality but with savings in assembly time. This is a step forward to making multi-kmer methods more reliable and easier to use.Availability and implementationA general implementation of our approach can be found under: https://github.com/SchulzLab/KREATIONSupplementary information: Supplementary data are available at Bioinformatics online.Contactmschulz@mmci.uni-saarland.de.

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