The transcriptomic analysis of the dehydration rate of mature rice seeds was conducted to explore candidate genes related to the dehydration rate and provide a theoretical basis for breeding and utilization. In this study, we selected Baghlani Nangarhar, an extremely fast dehydration material, and Saturn, a slow dehydration material, based on the results of the previous studies of screening of 165 germplasm materials for dehydration rate phenotypes. Fast dehydration experiment on these two types of seeds was conducted. Four comparative groups were set up under control and dehydration conditions. The differentially expressed genes (DEGs) were quantified by transcriptome sequencing and quantitative real-time PCR (qRT-PCR). GO and KEGG analyses were carried out. The results showed that in Baghlani Nangarhar, 53 DEGs were screened, of which 33 were up-regulated and 20 were down-regulated. Similarly, in Saturn, 25 DEGs were screened, of which 19 were up-regulated and 6 down-regulated. The results of the GO analysis showed that the sites of action of the differentially expressed genes enriched in the fast dehydration modes were concentrated in the cytoplasm, internal components of the membrane and nucleosomes. They play regulatory roles in catalysis, binding, translocation, transcription, protein folding, degradation and replication. They are involved in adaptive responses to adverse external environments such as reactive oxygen species and high temperature. The KEGG analysis showed that the main metabolic pathways enriched are protein processing in the endoplasmic reticulum, amino acid biosynthesis, and oxidative phosphorylation. The key differentially expressed genes and the most important metabolic pathways in the rapidly and slowly dehydrated materials obtained in this study were protein processing in the endoplasmic reticulum and oxidative phosphorylation metabolism, which are presumed to have important regulatory roles in stress/defense, energy metabolism, protein synthesis/folding, and signal transduction during dehydration and drying of mature seeds. The results of this study could potentially provide a valuable reference for further research on the genes and metabolic pathways related to the dehydration rate of mature rice seeds and provide theoretical guidance for the selection and breeding of new rice germplasm that can be rapidly dehydrated at the mature stage.

Lily database online genomic resource composed of Korean Lily Germplasm Collection, Transcriptome Sequences, molecular markers, transcription factors (TF) and DEGs data. A total of ~0.23 gb of RNA-sequences data were mined for gene identification, marker development and gene expression analysis. As a result, 47,863 SSR, 20,929 SNP and 1213 COS-marker were developed. A total of 1327 TF genes were identified and characterized. This is the first unique, user-friendly, genomic resource database for Lilium species. It is a relational database based on a ‘three-tier architecture’ that catalogs all the information in MySQL table and a user-friendly query interface and data visualization page developed using JavaScript, PHP and HTML code. The search parameters are highly flexible, user can be retrieved data by using either single or multiple search parameters. Data present in this database can be used for germplasm characterization, gene discovery, population structure analysis, QTL mapping and accelerating the lily variety improvements. This database can be accessed through this link: http://www.genomicsres.org/lilidb/.

Background: Identification of cancer biomarkers that are differentially expressed (DE) under two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, which does not satisfy the requirements of paired samples where the gene expressions are taken from the same patients before and after treatment. Furthermore, the traditional biomarker identification methods based on either p-values or fold change (FC) values. However, sometimes, p-value based results do not comply with FC based results due to the smaller variance of gene expressions. There are some methods that combine both p-values and FC values to solve this problem. But, these methods also show weak performance for small-sample case in presence of outlying expressions. To overcome this problem, in this paper an attempt is made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values based on SAM statistic using minimum β-divergence method, which is designed for paired samples. This method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual/normal expressions and smaller weights for unusual/outlying expressions. The β-weight function plays the significant role on the performance of the proposed method. Results: The proposed method uses β-weight function as a measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β-weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and FCROS) using both simulated and real gene expression profiles for both small-and large-sample cases. From the simulation and a real spike in data analysis results we observed that the proposed method outperforms other methods for small-sample case in presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these 2 genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank databases. Conclusion The identified drugs showed statistical significance and critical role of the proteins indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the associations between the drugs identified in the present study require further investigations.