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Title: An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions}
Article:
Abstract:
Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal( SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast( http://sage. fhcrc. org/downloads/downloads/predicted_yeast_genetic_interactions. zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.
Introduction:
Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function[1],[2], dissecting protein complexes into functional pathways[3] and exploring the sources underlying complex inherited human diseases[1]. In yeast, the systematic deletion of all genes( ∼6000) has been instrumental in delineating the non-essential genes that in combination with other gene mutations may lead to a loss of viability. However, testing all pair-wise combinations of these genes for genetic interactions under many different conditions is still prohibitive in terms of time and materials. Synthetic sickness and lethality( SSL) are the most studied types of genetic interactions in yeast. However, only a small portion of all possible SSL interactions have been uncovered under the limited contexts in which interactions were assessed[1]. To expand the set of known SSL interactions, several efforts have been undertaken to build models that predict genetic interactions, particularly SSL ones, in yeast and other organisms[4],[5],[6],[7],[8],[9],[10],[11]. The multiple network decision tree( MNDT) approach[8] represents the most comprehensive work to predict SSL interactions with a high precision. MNDT first extracted both SSL-dependent features( referred to as 2-hop features with at least one of the two networks being the known SSL interaction network), and SSL-independent features( the known SSL network is not involved) to train a decision tree-based classifier. Given two networks characterizing relationships between genes, a 2-hop feature for two genes A and B is used to represent whether there is a 2-step path between A and B through a third gene C with two links( A–C and B–C) in different networks[8]. For example, the physical-SSL relationship, where one link is from the protein interaction network and the other from the SSL interaction network, is an SSL-dependent feature since it involves the known SSL interactions. The results of MNDT showed that the most effective features differentiating SSL and non-SSL interactions were the 2-hop features derived from the overlay of the multiple networks, particularly when one of the networks was the known SSL network. However, the use of only SSL-independent features in this study led to a true positive rate that was less than 40% at a false positive rate of 20%. Given that only a small fraction of the total set of gene pairs in various organisms have been tested for SSL interactions, the SSL-dependent features will not be available for many of the remaining gene pairs, and thus, the prediction of those new pairs is expected to be made with a low accuracy. This problem will be exacerbated in higher organisms for which very few genetic interactions have been identified to date. Chipman and Singh extended the MNDT approach by utilizing the existing SL data, as well as several features from gene expression, protein interaction and GO functional annotation data, to predict SSL interactions among S. cerevisiae and C. elegans genes[4]. However, the primary focus of this work was to demonstrate that random walks on networks produced more effective features than 2-hop features for this prediction problem, and thus it did not help address the problem of dependence on SSL data for making novel SSL predictions. Particular emphasis was also not placed on how to best use the other features for addressing this problem. In a more specialized approach, Qi et al. focused on the network of SSL interactions between genes in S. cerevisiae, and used diffusion kernels defined on this network within an SVM classifier to predict several novel SSL interactions[7]. They also predicted several pairs of functionally associated genes that have a high likelihood of belonging to the same complexes, pathways and GO functional classes. However, this approach faces the same challenge as Wong et al. 's approach that it may not be effective for predicting genetic( SSL) interactions between genes that may not be well represented in the known SSL network. Among other efforts, Paladugu et al. focused on extracting multiple features only from protein interaction networks, and used them within an SVM classifier to predict SSL interactions[11]. Zhong and Sternberg[9] predicted genetic interactions between C. elegans genes using a machine learning approach. However, due to the limited amount of genome-wide data available for C. elegans, they also integrated features of orthologous gene pairs in yeast and fly. Although they made predictions for several C. elegans genes, they only provided estimates of the accuracy of their predictor using examples of specific pathways and biological systems, thus making a comprehensive estimation of the utility of biological features from C. elegans itself( organism-specific features) difficult. Overall, MNDT[8] remains one of the most extensive and effective organism-specific approach in the literature for extracting and integrating a wide variety of biological features for predicting genetic interactions and has been the basis for validating new algorithms[9]. In this paper, we build upon the existing approaches by developing a Multi-Network and Multi-Classifier( MNMC) framework that predicts SL interactions in yeast more effectively. The enhanced accuracy of the predictions is achieved by incorporating a more comprehensive set of SL-independent features that capture the relationships between genes, and simultaneously employing multiple classification procedures, thus leveraging the strengths of each while reducing the effects of their respective weaknesses. Since our method is based on SL-independent features, it is more appropriate for settings where very few gene pairs have been tested, including higher organisms where large-scale genetic interaction screens are not yet feasible. We further applied this approach to predict the genetic interactions between the known transcription factors( TFs) in S. cerevisiae and uncovered a number of novel SL interactions between TFs, which were well supported by the available knowledge about these TFs. We further expanded this effort by predicting genetic interactions among approximately 7. 5 million pairs of non-essential S. cerevisiae genes, the results of which are available at http://sage. fhcrc. org/downloads/downloads/predicted_yeast_genetic_interactions. zip. The details of these results and the materials and methods used can be found in the subsequent sections. However, before that discussion, we would like to note that while we have focused on the prediction of SL interactions, we demonstrate that our approach is also capable of predicting other categories of genetic interactions, like synthetic sickness.
Discussion:
We devised an integrative, multi-network approach for predicting synthetic lethal( SL) interactions, which extends the previously proposed MNDT approach[8]. In this approach, we first defined a large number of features for characterizing the relationships between pairs of genes, and then developed a multi-classifier system for predicting whether a given gene pair belongs to the SL or non-SL class. Comprehensive experiments on several data sets demonstrated that these features, in conjunction with the advanced classification scheme, led to an improved performance when compared to the current state of the art method. In particular, a large number of features we identified were independent of the known SL interactions( in contrast to MNDT), and these were shown to be more effective for making predictions for gene pairs that are not well connected with the known SL interactions. Application of this approach to the known transcription factor pairs led to the first TF SL interaction network. Several of the predicted synthetic lethal interactions between transcription factors are well supported by literature. It is of note that all the SL data used in this paper was obtained in rich media, therefore most of the predicted SL interactions are expected to refer to rich media conditions. However, here we showed that some condition-specific SL interactions can also be predicted by our approach. This may reflect the fact that some features such as functional annotation, gene expression signatures, and sequence similarity can help identify such condition-specific interactions. Our approach is expected to be effective for uncovering new genetic interactions among millions of gene pairs in yeast and hundreds of millions of gene pairs in higher organisms like mouse and human that have not been tested for these interactions. In addition, this type of predictor could have utility even after comprehensive empirical screens have been carried out, given that the effects leading to genetic interactions may very well be context-dependent and it may not be feasible to experimentally assess all interactions under all contexts. Given the difficulty of the problem of predicting genetic interactions, even the best classification methods suffer low precision and low coverage. On the other hand, this also opens the door for exploring new methods. The fact that the features independent of the known genetic interaction( GI) network can better predict gene pairs less connected in the GI network while the whole features including both GI dependent and independent features lead to better performance on the pairs well connected in the GI network, suggests a new avenue to further improve the prediction performance: use the connectivity of each candidate gene pair in the known GI network to select an appropriate classifier. More generally, we can partition GI interactions into different groups based on features characterizing gene pairs and then train a classifier for each group. Such ideas are worth exploring in future work on this important problem.
Genetic interactions occur when a combination of mutations results in a surprising phenotype . These interactions capture functional redundancy , and thus are important for predicting function , dissecting protein complexes into functional pathways , and exploring the mechanistic underpinnings of common human diseases . Due to the large number of possible combinations of genes , only a small portion of gene pairs in yeast and only a few pairs in higher organisms like mouse and human have been tested . Therefore , predicting genetic interactions has received significant attention in the past several years . The existing methods primarily rely on the known genetic interactions , and thus are far less effective in classifying most gene pairs not well connected with known genetic interactions . Here we developed a non-parametric multi-classifier system for predicting genetic interactions based on a large number of novel features independent of the known genetic interactions . This approach led to an improved performance when compared to the current state-of-the-art method . Using this approach , we derived the first yeast transcription factor genetic interaction network , part of which was well supported by literature . This integrative approach is expected to be more effective and robust in uncovering new genetic interactions in yeast and other species .