Global transcriptome profiles of Camellia sinensis during cold acclimation.

BACKGROUND: Tea is the most popular non-alcoholic health beverage in the world. The tea plant (Camellia sinensis (L.) O. Kuntze) needs to undergo a cold acclimation process to enhance its freezing tolerance in winter. Changes that occur at the molecular level in response to low temperatures are poorly understood in tea plants. To elucidate the molecular mechanisms of cold acclimation, we employed RNA-Seq and digital gene expression (DGE) technologies to the study of genome-wide expression profiles during cold acclimation in tea plants. RESULTS: Using the Illumina sequencing platform, we obtained approximately 57.35 million RNA-Seq reads. These reads were assembled into 216,831 transcripts, with an average length of 356 bp and an N50 of 529 bp. In total, 1,770 differentially expressed transcripts were identified, of which 1,168 were up-regulated and 602 down-regulated. These include a group of cold sensor or signal transduction genes, cold-responsive transcription factor genes, plasma membrane stabilization related genes, osmosensing-responsive genes, and detoxification enzyme genes. DGE and quantitative RT-PCR analysis further confirmed the results from RNA-Seq analysis. Pathway analysis indicated that the "carbohydrate metabolism pathway" and the "calcium signaling pathway" might play a vital role in tea plants' responses to cold stress. CONCLUSIONS: Our study presents a global survey of transcriptome profiles of tea plants in response to low, non-freezing temperatures and yields insights into the molecular mechanisms of tea plants during the cold acclimation process. It could also serve as a valuable resource for relevant research on cold-tolerance and help to explore the cold-related genes in improving the understanding of low-temperature tolerance and plant-environment interactions.

Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study.

BACKGROUND: With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data. RESULTS: To reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies. CONCLUSIONS: Our work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.