Transcriptomic analysis of a wild and a cultivated varieties of Capsicum annuum over fruit development and ripening.

Chili pepper (Capsicum annuum) is one of the most important crops worldwide. Its fruits contain metabolites produced over the maturation process like capsaicinoids and carotenoids. This metabolic process produces internal changes in flavor, color, texture, and aroma in fruits to make them more attractive for seed dispersal organisms. The chiltepin (C. annuum L. var. glabriusculum) is a wild variety of the C. annuum L. species that is considered a source of genetic resources that could be used to improve the current chili crops. In this study, we performed a transcriptomic analysis on two fruit maturation stages: immature stage (green fruit) and mature stage (red fruit) of a wild and a cultivated pepper variety. We found 19,811 genes expressed, and 1,008 genes differentially expressed (DEGs) in at least one of the five contrast used; 730 DEGs were found only in one contrast, and most DEGs in all contrasts were downregulated. GO enrichment analysis showed that the majority of DEGs are related to stress responses. KEGG enrichment analysis detected differences in expression patterns in metabolic pathways related to phenylpropanoid biosynthesis, secondary metabolites, plant hormone signal transduction, carotenoid biosynthesis and sesquiterpenoid and triterpenoid biosynthesis. We selected 105 tomato fruit ripening-related genes, and found 53 pepper homologs differentially expressed related to shape, size, and secondary metabolite biosynthesis. According to the transcriptome analysis, the two peppers showed very similar gene expression patterns; differences in expression patterns of genes related to shape, size, ethylene and secondary metabolites biosynthesis suggest that changes produced by domestication of chilli pepper could be very specific to the expression of genes related to traits desired in commercial fruits.

Transcriptome Analyses Throughout Chili Pepper Fruit Development Reveal Novel Insights into the Domestication Process.

Chili pepper (Capsicum spp.) is an important crop, as well as a model for fruit development studies and domestication. Here, we performed a time-course experiment to estimate standardized gene expression profiles with respect to fruit development for six domesticated and four wild chili pepper ancestors. We sampled the transcriptomes every 10 days from flowering to fruit maturity, and found that the mean standardized expression profiles for domesticated and wild accessions significantly differed. The mean standardized expression was higher and peaked earlier for domesticated vs. wild genotypes, particularly for genes involved in the cell cycle that ultimately control fruit size. We postulate that these gene expression changes are driven by selection pressures during domestication and show a robust network of cell cycle genes with a time shift in expression, which explains some of the differences between domesticated and wild phenotypes.

Compacta: a fast contig clustering tool for de novo assembled transcriptomes.

BACKGROUND: RNA-Seq is the preferred method to explore transcriptomes and to estimate differential gene expression. When an organism has a well-characterized and annotated genome, reads obtained from RNA-Seq experiments can be directly mapped to that genome to estimate the number of transcripts present and relative expression levels of these transcripts. However, for unknown genomes, de novo assembly of RNA-Seq reads must be performed to generate a set of contigs that represents the transcriptome. These contig sets contain multiple transcripts, including immature mRNAs, spliced transcripts and allele variants, as well as products of close paralogs or gene families that can be difficult to distinguish. Thus, tools are needed to select a set of less redundant contigs to represent the transcriptome for downstream analyses. Here we describe the development of Compacta to produce contig sets from de novo assemblies. RESULTS: Compacta is a fast and flexible computational tool that allows selection of a representative set of contigs from de novo assemblies. Using a graph-based algorithm, Compacta groups contigs into clusters based on the proportion of shared reads. The user can determine the minimum coverage of the contigs to be clustered, as well as a threshold for the proportion of shared reads in the clustered contigs, thus providing a dynamic range of transcriptome compression that can be adapted according to experimental aims. We compared the performance of Compacta against state of the art clustering algorithms on assemblies from Arabidopsis, mouse and mango, and found that Compacta yielded more rapid results and had competitive precision and recall ratios. We describe and demonstrate a pipeline to tailor Compacta parameters to specific experimental aims. CONCLUSIONS: Compacta is a fast and flexible algorithm for the determination of optimum contig sets that represent the transcriptome for downstream analyses.