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dc.contributor.authorKlein, A
dc.contributor.authorHusselmann, L.H.H
dc.contributor.authorWilliams, A
dc.date.accessioned2021-04-21T08:05:14Z
dc.date.available2021-04-21T08:05:14Z
dc.date.issued2021
dc.identifier.citationKlein, A. et al. (2021). Proteomic identification and meta-analysis in salvia hispanica RNA-seq de novo assemblies. Plants ,10(4),765en_US
dc.identifier.issn2223-7747
dc.identifier.uri10.3390/plants10040765
dc.identifier.urihttp://hdl.handle.net/10566/6094
dc.description.abstractWhile proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity “genes” varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.en_US
dc.language.isoenen_US
dc.subjectProteogenomicsen_US
dc.subjectSalvia hispanicaen_US
dc.subjectSalvia columbariaeen_US
dc.subjectNonmodel organismsen_US
dc.subjectBioinformaticsen_US
dc.titleProteomic identification and meta-analysis in salvia hispanica RNA-seq de novo assembliesen_US
dc.typeArticleen_US


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