Gene Essentiality Analysis Based on DEG, a Database of Essential Genes

Zhang, Chun‐Ting; Zhang, Ren

doi:10.1007/978-1-59745-321-9_27

Cited by 29 publications

(16 citation statements)

References 22 publications

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“…In COGs, genes can be generally classified into four broad functional categories: information storage & processing, cellular processes & signaling, metabolism and poorly characterized. Previous work has shown that essential genes are overrepresented in the category of information storage and processing with basic cellular functions such as RNA processing and modification and DNA replication [32]. Essential genes involved in this category are often well conserved across species.…”

Section: Resultsmentioning

confidence: 99%

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

et al. 2011

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Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

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Section: Resultsmentioning

confidence: 99%

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

et al. 2011

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“…Housekeeping and essential gene networks, as controls for nondisease networks, were built from 2,164 seed genes and their neighbor nodes (2064 for housekeeping and 115 for essential genes) from previous attempts [22,23]. Figure 2B shows the background distribution of similarity values to compute the p values of the disease–disease similarity value, μ.…”

Section: Resultsmentioning

confidence: 99%

Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions

et al. 2014

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BackgroundHuman diseases frequently cause complications such as obesity-induced diabetes and share numbers of pathological conditions, such as inflammation, by dysfunctions of common functional modules, such as protein–protein interactions (PPIs).MethodsOur developed pipeline, ICod (Interaction analysis for disease Comorbidity), grades similarities between pairs of disease-related PPIs including comorbid diseases and pathological conditions. ICod displayed a disease similarity network consisting of nodes of disease PPIs and edges of similarity value. As a proof of concept, eight complex diseases and pathological conditions, such as type 2 diabetes, obesity, inflammation, and cancers, were examined to discover whether PPIs shared between diseases were associated with comorbidities.ResultsBy comparing Medicare reports of disease co-occurrences from 31 million patients, the disease similarity network shows that PPIs of pathological conditions, including insulin resistance, and inflammation, overlap significantly with PPIs of various comorbid diseases, including diabetes, obesity, and cancers (p < 0.05). Interestingly, maintaining connectivity between essential genes was more drastically perturbed by removing a node of a disease-related gene rather than a pathological condition-related gene, such as one related to inflammations.ConclusionThus, PPIs of pathological symptoms are underlying functional modules across diseases accompanying comorbidity phenomena, whereas they contribute only marginally to maintaining interactions between essential genes.

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“…Our findings on these highly important features for essentiality are concordant with previous studies as follows. First, essential genes tend to be localized in the nucleus, where essential biological processes such as information storage and processing take place (4,16). Second, essential genes are known to be more evolutionarily-conserved than are nonessential genes in bacteria (3) as well as some eukaryotes (17).…”

Section: Resultsmentioning

confidence: 99%

Partial AUC maximization for essential gene prediction using genetic algorithms

Hwang

Ha²,

Ju³

et al. 2013

BMB Reports

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Identifying genes indispensable for an organism‘s life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, proteinprotein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature’s relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods. [BMB Reports 2013; 46(1): 41-46]

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Gene Essentiality Analysis Based on DEG, a Database of Essential Genes

Cited by 29 publications

References 22 publications

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions

Partial AUC maximization for essential gene prediction using genetic algorithms

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