Computational identification of human long intergenic non-coding RNAs using a GA–SVM algorithm

Long intergenic non-coding RNAs (lincRNAs) are a new type of non-coding RNAs and are closely related with the occurrence and development of diseases. In previous studies, most lincRNAs have been identified through next-generation sequencing. Because lincRNAs exhibit tissue-specific expression, the reproducibility of lincRNA discovery in different studies is very poor. In this study, not including lincRNA expression, we used the sequence, structural and protein-coding potential features as potential features to construct a classifier that can be used to distinguish lincRNAs from non-lincRNAs. The GA–SVM algorithm was performed to extract the optimized feature subset. Compared with several feature subsets, the five-fold cross validation results showed that this optimized feature subset exhibited the best performance for the identification of human lincRNAs. Moreover, the LincRNA Classifier based on Selected Features (linc-SF) was constructed by support vector machine (SVM) based on the optimized feature subset. The performance of this classifier was further evaluated by predicting lincRNAs from two independent lincRNA sets. Because the recognition rates for the two lincRNA sets were 100% and 99.8%, the linc-SF was found to be effective for the prediction of human lincRNAs.     

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome‐wide rice protein–protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet ) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non‐transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein–protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain‐mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet‐based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2–11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.     

LincRNA的研究进展

基因间长链非编码RNA(large intergenic non-coding RNA,linc RNA)是指基因间不编码蛋白质的长度大于200nt的RNA。最初,linc RNA被认为是基因组转录的"噪声",不具备任何生物学功能。然而,随着第二代测序技术的发展及其所产生的大量数据,越来越多的linc RNA被识别,引起了人们的重视。至今,已有超过12000的linc RNAs被收录在人类基因组中。随着研究的深入,linc RNA的生物机制和潜在的一些功能越来越明朗。本篇综述总结了近年来linc RNA的研究历程,序列特征,疾病中发挥的作用以及相关数据库的分析。