miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature

Background  

MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

Computational identification of microRNA targets

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.     

MicroRNA在肿瘤发生发展和诊断中作用的研究进展

microRNA是一类由内源基因编码的长度约为18-25个核苷酸的非编码单链RNA分子,可以与靶基因mRNA的3'非编码区结合,通过降解靶m RNA或(和)抑制靶m RNA转录后翻译调节靶蛋白的生成,从而发挥其生物学作用。目前,在人体基因组内发现的microRNA已经超过2500多个,可能调节着人类1/3的基因,在维持正常干细胞功能、调控细胞增殖分化及恶性肿瘤发生过程中均起重要作用。既往的研究表明microRNA与基因之间相互调控的失衡导致肿瘤的发生。从分子水平上研究microRNA与肿瘤发生的关系,检测microRNA与肿瘤相关基因表达情况的改变,分析肿瘤组织和血清中microRNA表达量与肿瘤分型的关系,将有利于肿瘤的病因学研究,早期发现和肿瘤治疗及预后判断。本文主要就microRNA在肿瘤发生发展和诊断中作用的研究进展进行了综述。     

Prediction of personalized microRNA activity

MicroRNAs have been known to regulate almost all physiological and pathological processes by suppressing their target genes. In humans, more than 1000 microRNAs have been identified, each of which targets dozens or even hundreds of genes. Facing this huge repertoire of microRNA targeting, it is important to identify which microRNAs are active, i.e., down-regulating their targets, in specific physiological or pathological conditions. Predicting active microRNAs is different from predicting microRNA targets because the authentic target genes of a microRNA are often not directly and solely regulated by that microRNA, leading to inconsistent expression changes between the microRNA and its true targets. Several computational programs have been proposed to predict the activity of a microRNA from the expressions of its target genes. These programs performed well when being applied on the expression data obtained from distinct tissue types or from experiments that transfect a microRNA into cells (i.e., non-physiological). But the performance of microRNA activity prediction is not clear on the expression data from the same tissue type in two physiological conditions, e.g., liver tissues from cancer patients and healthy people. In this work, we evaluate the performance of two microRNA activity prediction programs using seven expression data sets, all of which compare samples in two physiological conditions, as well as propose a new approach that predicts microRNA activity with an accuracy of over 80%. Unlike current methods, which predict active microRNAs by comparing two groups of samples, e.g., tumor versus normal, our new approach compares each diseased sample with all the samples in the control group. In other words, it can predict the microRNA activity of a person. In this work, this new application is named to predict “personalized microRNA activity”.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Diverse correlation patterns between microRNAs and their targets during tomato fruit development indicates different modes of microRNA actions

MicroRNAs negatively regulate the accumulation of mRNAs therefore when they are expressed in the same cells their expression profiles show an inverse correlation. We previously described one positively correlated miRNA/target pair, but it is not known how widespread this phenomenon is. Here, we investigated the correlation between the expression profiles of differentially expressed miRNAs and their targets during tomato fruit development using deep sequencing, Northern blot and RT-qPCR. We found an equal number of positively and negatively correlated miRNA/target pairs indicating that positive correlation is more frequent than previously thought. We also found that the correlation between microRNA and target expression profiles can vary between mRNAs belonging to the same gene family and even for the same target mRNA at different developmental stages. Since microRNAs always negatively regulate their targets, the high number of positively correlated microRNA/target pairs suggests that mutual exclusion could be as widespread as temporal regulation. The change of correlation during development suggests that the type of regulatory circuit directed by a microRNA can change over time and can be different for individual gene family members. Our results also highlight potential problems for expression profiling-based microRNA target identification/validation.     

基于TransformerMGI的microRNA靶向基因预测

生物小分子microRNA可以对基因表达进行正向或负向调控,研究microRNA与基因之间的关系对于机体稳态的维持和疾病治疗都有着重要意义。利用深度学习方法对microRNA和基因靶向关系进行预测,提出了TransformerMGI模型。在特征工程阶段,针对生物序列潜在信息难以准确地提取这一问题,TransformerMGI模型分别采用了基于图卷积神经网络的GP-GCN方法和DNA2Vec模型对microRNA和基因数据的潜在信息进行提取,得到了二者的表征嵌入矩阵,在模型方面,TransformerMGI模型引入了幂归一化来改进经典的深度学习模型。利用microRNA和基因数据经过特征提取后得到两个表征矩阵,这两个矩阵分别被放入TransformerMGI模型中,通过TransformerMGI模型内部的Attention机制对二者自身和相互的特征信息进行了聚合和关联运算,最终预测出microRNA调控基因的概率。采用ROC曲线下面积和准确召回率曲线作为模型性能评价指标,将TransformerMGI与其他现有模型进行了比较评估。实验结果表明,TransformerMGI模型的AUC和AUPRC评分均可达0.91以上,优于现有的其他模型。TransformerMGI模型能在不考虑生物学原理和基因组背景的前提下,仅依赖microRNA和基因的碱基序列信息,实现microRNA靶向基因的预测,从而为后续的microRNA靶向基因预测研究提供了可借鉴的深度学习方法。     

A probabilistic approach to microRNA-target binding

Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new sequence and defines the likelihood of this sequence using a Variable Length Markov Chain. It offers a complementary representation of microRNA–mRNA pairs for microRNA target prediction tools or other probabilistic frameworks of integrative gene regulation analysis. The performance of present model is evaluated by its ability to predict microRNA–target mRNA interaction given a mature microRNA sequence and a putative mRNA binding site. In regard to classification accuracy, it outperforms two recent methods based on thermodynamic stability and sequence complementarity. The experiments can also unveil the effects of base pairing types and non-seed region in duplex formation.