Back

In silico comparative RNA-seq analysis reveals varietal-specific intergenic small open reading frames in Cucumis sativus L.

Chieng, G. S. W.; Tan, B. C.; Teo, C. H.

2022-10-17 bioinformatics
10.1101/2022.10.13.512045 bioRxiv
Show abstract

Small open reading frames (sORFs) have been reported to play important roles in growth, regulation of morphogenesis, and abiotic stress responses in various plant species. However, their sequences and functions remain poorly understood in many plant species including Cucumis sativus. Cucumis sativus (commonly known as cucumber) is Asias fourth most important vegetable and the second most important crop in Western Europe. The breeding of climate-resilient cucumbers is of great importance to ensure their sustainability under extreme climate conditions. In this study, we aim to isolate the intergenic sORFs from C. sativus var. hardwickii genome and determine their sequence diversity and expression profiles in C. sativus var. hardwickii and different cultivars of C. sativus var. sativus using bioinformatics tools. We identified a total of 50,191 coding sORFs with coding potential (coding sORFs) from C. sativus var. hardwickii genome. In addition, 1,311 transcribed sORFs were detected in RNA-seq datasets of C. sativus var. hardwickii and shared homology to sequences deposited in the cucumber EST database, and among these, 91 transcribed sORFs with translation potential were detected. A total of 629 high-confident C. sativus-specific sORFs were identified in both varieties. Varietal-specific transcribed sORFs were also identified in C. sativus var. hardwickii (87) and C. sativus var. sativus (2,906). Furthermore, cultivar- and tissue-specific transcribed sORFs were identified in different cultivars and tissue samples. The findings of this study provide insight into sequence diversity and expression patterns of sORFs in C. sativus, which could help in developing climate-resilient cucumbers.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.