拟南芥基因组中新的microRNA预测及分析

MicroRNA（miRNA）是一类存在于动植物体内、长度为21～25nt的内源性小RNA,对生物体的转录后基因调控起着关键作用,但一些低丰度的miRNA和组织特异性miRNA往往很难发现。为了系统识别拟南芥基因组中新的非同源miRNA,首先基于已报道的拟南芥miRNA的特征,从全基因组范围中筛选出453条可能的miRNA前体;其次,为了进一步对上述miRNA前体进行筛选,利用人的miRNA前体数据构建了支持向量机模型GenomicSVM,该模型对人测试集的敏感性和特异性分别为86.3％和98．1％（30个人miRNA前体和1000个阴性miRNA前体）,对拟南芥测试集的正确率为93．6％（78个miRNA前体）;最后,利用GenomicSVM预测上述453条miRNA前体序列,得到了37条候选的新的拟南芥miRNA前体,为进一步的miRNA实验发现研究提供了指导。     

拟南芥CRKs家族自抑域序列的预测

目的:以拟南芥CDPKs激酶家族为摹本,推测类结构激酶家族CRKs的自抑域.方法:以生物信息学工具phylip3.65和clustalx1.83对CDPK家族和CRK家族进行进化树构建平和多重序列比对分析.结果:结果显爪CDPK亚家族Ⅳ与CRK家族的相似程度很高,且自抑域序列区域非常保守.以此为依据预测了CRK家族各成员的自抑域序列.     

Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE

Background

Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits.

Methodology

In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility.

Conclusions

We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studying genotype-phenotype relationships. The software is freely available with source code at http://ccst.jlu.edu.cn/JCSB/RFET/.     

Differential accumulation of the transcripts of 22 novel protein kinase genes in Arabidopsis thaliana

22 novel members of the Arabidopsis thaliana protein kinase family (AKs) were identified by using degenerate oligonucleotide primers directed to highly conserved amino acid sequences of the protein kinase (PK) catalytic domain. Of these 22 genes, 16 turned out to carry intron sequences. Homologies of AK sequences were detected to S-locus receptor protein kinases (SRKs) from Brassica spp., to SRK-like PKs from maize and A. thaliana and to several other receptor PKs from A. thaliana. Sequence similarity was also detected to Ca²⁺-dependent PKs (CDPKs) from rape and soybean, to SNF1 and to CDC2 homologues. The genomic organization and the accumulation of the mRNAs from these 22 AK genes were investigated.     

Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Ralstonia solanacearum is a devastating bacterial pathogen that has an unusually wide host range. R. solanacearum, together with Arabidopsis thaliana, has become a model system for studying the molecular basis of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in the infection process, and some PPIs can initiate a plant defense response. However, experimental investigations have rarely addressed such PPIs. Using two computational methods, the interolog and the domain-based methods, we predicted 3,074 potential PPIs between 119 R. solanacearum and 1,442 A. thaliana proteins. Interestingly, we found that the potential pathogen-targeted proteins are more important in the A. thaliana PPI network. To facilitate further studies, all predicted PPI data were compiled into a database server called PPIRA (http://protein.cau.edu.cn/ppira/). We hope that our work will provide new insights for future research addressing the pathogenesis of R. solanacearum.     

Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers

A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.