2012
DOI: 10.1186/1471-2164-13-640
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Generation and analysis of expressed sequence tags in the extreme large genomes Lilium and Tulipa

Abstract: BackgroundBulbous flowers such as lily and tulip (Liliaceae family) are monocot perennial herbs that are economically very important ornamental plants worldwide. However, there are hardly any genetic studies performed and genomic resources are lacking. To build genomic resources and develop tools to speed up the breeding in both crops, next generation sequencing was implemented. We sequenced and assembled transcriptomes of four lily and five tulip genotypes using 454 pyro-sequencing technology.ResultsSuccessfu… Show more

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Cited by 73 publications
(94 citation statements)
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“…To get a better impression of the quality and completeness of the functional annotated datasets, we compared our transcriptomes and annotation with previously published transcriptomes of tulip and lily (Shahin et al 2012b). Initially, we performed a BLAST search at the nucleotide level to determine how well we covered the transcripts present in these publicly available datasets.…”
Section: Functional Annotationmentioning
confidence: 99%
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“…To get a better impression of the quality and completeness of the functional annotated datasets, we compared our transcriptomes and annotation with previously published transcriptomes of tulip and lily (Shahin et al 2012b). Initially, we performed a BLAST search at the nucleotide level to determine how well we covered the transcripts present in these publicly available datasets.…”
Section: Functional Annotationmentioning
confidence: 99%
“…The number of transcripts and predicted genes in our non-filtered transcriptomes may be highly over-estimated taking into account that there are only 27,024 protein coding gene models in the recently sequenced monocot genome of pineapple (Ming et al 2015), 39,045 genes reported for rice-the monocot model species-(Sequencing ProjectInternational Rice 2005) and 81,791 for tulip, based on a previous transcriptome sequencing effort (Shahin et al 2012b). …”
Section: Filtering Out Transcripts With Low Abundance Reduced the Nummentioning
confidence: 99%
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