Biology subjects

Qu, H.

Publications and source records attributed to Qu, H..

40 records · Page 3Linked to original sources

bioRxiv2018

RNA-Seq analysis of compatible and incompatible styles of Pyrus species at the beginning of pollination

In Rosaceae, incompatible pollen can penetrate into the style during the gametophytic self-incompatibility response. It is therefore considered a stylar event rather than a stigmatic event. In this study, we explored the differences in gene expression between compatibility and incompatibility in the early stage of pollination. The self-compatible pear variety \"Jinzhuili\" is a naturally occurring bud mutant from \"Yali\", a leading Chinese native cultivar exhibiting typical gametophytic self-incompatibility. We collected the styles of Yali and Jinzhuili at 0.5 and 2 h after self-pollination and then performed high-throughput sequencing. According to the pathway enrichment analysis of the differentially expressed genes, \"plant-pathogen interaction\" was the most represented pathway. Quantitative PCR was used to validate these differential genes. The expression levels of genes related to pollen growth and disease inhibition, such as LRR (LEUCINE-RICH REPEAT EXTENSIN), resistance, and defensin, differed significantly between compatible and incompatible pollination. Interestingly, at 0.5 h, most of these genes were upregulated in the compatible pollination system compared with the incompatible pollination system. Calcium ion transport, which requires ATPase, also demonstrated upregulated expression. In summary, the self-incompatibility reaction was initiated when the pollen came into contact with the stigma.

plant biology↗

bioRxiv2018

Optimizing Trait Predictability in Hybrid Rice Using Superior Prediction Models and Selective Omic Datasets

Hybrid breeding has dramatically boosted yield and its stability in rice. Genomic prediction further benefits rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting genetic values (or breeding values) for agronomically important traits. In the current study, we searched for the best prediction strategy for four traits (yield, 1000 grain weight, number of grains per panicle and number of tillers per plant) of hybrid rice by evaluating all possible combinations of omic datasets with different prediction methods. We conclude that, in rice, the predictions using the combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Inclusion of transcriptomic data does not improve predictability possibly because transcriptome does not provide more information for the trait than the sum of genome and metabolome; rather, the computational complexity is substantially increased if transcriptomic data is included in the models. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including LASSO, SSVS, SVM-RBF, SVP-POLY and PLS. Our study has provided a guideline for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability.

genetics↗

bioRxiv2017

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, to facilitate downloading, organizing, and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs (ceRNAs) regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis.\n\nAvailabilityGDCRNATools is an R/Bioconductor package that is freely available at https://github.com/Jialab-UCR/GDCRNATools

bioinformatics↗

bioRxiv2017

A novel method for large-scale identification of polymorphic microsatellites through comparative transcriptome analysis

Microsatellite (SSR) is one of the most popular markers for applied genetic research, but generally the current methods to develop SSRs are relatively time-consuming and expensive. Although high-throughput sequencing (HTS) approach has become a practical and relatively inexpensive option so far, only a small percentage of SSR markers turn out to be polymorphic. Here, we designed a new method to enrich polymorphic SSRs through the comparative transcriptome analysis. This program contains five main steps: 1) transcriptome data downloading or RNA-seq; 2) sequence assembly; 3) SSR mining and enrichment of sequences containing SSRs; 4) sequence alignment; 5) enrichment of sequences containing polymorphic SSRs. A validation experiment was performed and the results showed almost all markers (> 90%) that were indicated as putatively polymorphic by this method were indeed polymorphic. The frequency of polymorphic SSRs was significantly higher (P < 0.05) but the cost and running time were much lower than those of traditional and HTS approaches. The method has a practical value for polymorphic SSRs development and might be widely used for genetic analyses in any species.

bioinformatics↗