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A new computational strategy for predicting essential genes.

ABSTRACT: BACKGROUND: Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. RESULTS: We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. CONCLUSIONS: FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

SUBMITTER: Cheng J

PROVIDER: S-EPMC3880044 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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A new computational strategy for predicting essential genes.

Cheng Jian J Wu Wenwu W Zhang Yinwen Y Li Xiangchen X Jiang Xiaoqian X Wei Gehong G Tao Shiheng S

BMC genomics 20131221

<h4>Background</h4>Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms.<h4>Results</h4>We first collected features that we ...[more]

PMID: 24359534

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Project description:Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same cost-sensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.

Project description:The identification of microbial genes essential for survival as those with lethal knockout phenotype (LKP) is a common strategy for functional interrogation of genomes. However, interpretation of the LKP is complicated because a substantial fraction of the genes with this phenotype remains poorly functionally characterized. Furthermore, many genes can exhibit LKP not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes (conditionally essential genes). We analyzed the sets of LKP genes for two archaea, Methanococcus maripaludis and Sulfolobus islandicus, using a variety of computational approaches aiming to differentiate between essential and conditionally essential genes and to predict at least a general function for as many of the proteins encoded by these genes as possible. This analysis allowed us to predict the functions of several LKP genes including previously uncharacterized subunit of the GINS protein complex with an essential function in genome replication and of the KEOPS complex that is responsible for an essential tRNA modification as well as GRP protease implicated in protein quality control. Additionally, several novel antitoxins (conditionally essential genes) were predicted, and this prediction was experimentally validated by showing that the deletion of these genes together with the adjacent genes apparently encoding the cognate toxins caused no growth defect. We applied principal component analysis based on sequence and comparative genomic features showing that this approach can separate essential genes from conditionally essential ones and used it to predict essential genes in other archaeal genomes.IMPORTANCEOnly a relatively small fraction of the genes in any bacterium or archaeon is essential for survival as demonstrated by the lethal effect of their disruption. The identification of essential genes and their functions is crucial for understanding fundamental cell biology. However, many of the genes with a lethal knockout phenotype remain poorly functionally characterized, and furthermore, many genes can exhibit this phenotype not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes. We applied state-of-the-art computational methods to predict the functions of a number of uncharacterized genes with the lethal knockout phenotype in two archaeal species and developed a computational approach to predict genes involved in essential functions. These findings advance the current understanding of key functionalities of archaeal cells.

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A new computational strategy for predicting essential genes.

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A new computational strategy for predicting essential genes.

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