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1.
Front Plant Sci ; 12: 667678, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34354718

RESUMO

Chia (Salvia hispanica L.), now a popular superfood and a pseudocereal, is one of the richest sources of dietary nutrients such as protein, fiber, and polyunsaturated fatty acids (PUFAs). At present, the genomic and genetic information available in the public domain for this crop are scanty, which hinders an understanding of its growth and development and genetic improvement. We report an RNA-sequencing (RNA-Seq)-based comprehensive transcriptome atlas of Chia sampled from 13 tissue types covering vegetative and reproductive growth stages. We used ~355 million high-quality reads of total ~394 million raw reads from transcriptome sequencing to generate de novo reference transcriptome assembly and the tissue-specific transcript assemblies. After the quality assessment of the merged assemblies and implementing redundancy reduction methods, 82,663 reference transcripts were identified. About 65,587 of 82,663 transcripts were translated into 99,307 peptides, and we were successful in assigning InterPro annotations to 45,209 peptides and gene ontology (GO) terms to 32,638 peptides. The assembled transcriptome is estimated to have the complete sequence information for ~86% of the genes found in the Chia genome. Furthermore, the analysis of 53,200 differentially expressed transcripts (DETs) revealed their distinct expression patterns in Chia's vegetative and reproductive tissues; tissue-specific networks and developmental stage-specific networks of transcription factors (TFs); and the regulation of the expression of enzyme-coding genes associated with important metabolic pathways. In addition, we identified 2,411 simple sequence repeats (SSRs) as potential genetic markers from the transcripts. Overall, this study provides a comprehensive transcriptome atlas, and SSRs, contributing to building essential genomic resources to support basic research, genome annotation, functional genomics, and molecular breeding of Chia.

2.
Mol Cell Proteomics ; 9(8): 1689-702, 2010 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-20507923

RESUMO

Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments with data from established structure determination techniques. Correspondingly, integrative computational methods are being developed to provide descriptions of protein complexes at varying levels of accuracy and resolution, ranging from complex compositions to detailed atomic structures.


Assuntos
Modelos Moleculares , Complexos Multiproteicos/metabolismo , Proteômica/métodos , Humanos , RNA Polimerase II/química , RNA Polimerase II/metabolismo
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