NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks.
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ABSTRACT:
SUBMITTER: Senthamizhan V
PROVIDER: S-EPMC8495214 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.
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Project description:Addictions are chronic and common brain disorders affected by many genetic, environmental, and behavioral factors. Recent genome-wide linkage and association studies have revealed several promising genomic regions and multiple genes relating to addictions. To explore the underlying biological processes in the development of addictions, we used 62 genes recently reviewed by Li and Burmeister (2009) as representative addiction-related genes, and then we investigated their features in gene function, pathways, and protein interaction networks. We performed enrichment tests of their Gene Ontology (GO) annotations and of their pathways in the Ingenuity Pathways Analysis (IPA) system. The tests revealed that these addiction-related genes were highly enriched in neurodevelopment-related processes. Interestingly, we found circadian rhythm signaling in one of the enriched pathways. Moreover, these addiction-related genes tended to have higher connectivity and shorter characteristic shortest-path distances compared to control genes in the protein-protein interaction (PPI) network. This investigation is the first of such kind in addiction studies, and it is useful for further addiction candidate-gene prioritization and verification, thus helping us to better understand molecular mechanisms of addictions.
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Project description:Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29-47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.
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Project description:MOTIVATION: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug-target interactions is crucial in the drug design process. RESULTS: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug-target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L(1) regularized classifiers over the tensor product space of possible drug-target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug-target interactions and the extracted features are biologically meaningful. The extracted substructure-domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. AVAILABILITY: Softwares are available at the supplemental website. CONTACT: yamanishi@bioreg.kyushu-u.ac.jp SUPPLEMENTARY INFORMATION: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ .
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Project description:UnlabelledRubrivivax gelatinosus is a betaproteobacterium with impressive metabolic diversity. It is capable of phototrophy, chemotrophy, two different mechanisms of sugar metabolism, fermentation, and H2 gas production. To identify core essential genes, R. gelatinosus was subjected to saturating transposon mutagenesis and high-throughput sequencing (TnSeq) analysis using nutrient-rich, aerobic conditions. Results revealed that virtually no primary metabolic genes are essential to the organism and that genomic redundancy only explains a portion of the nonessentiality, but some biosynthetic pathways are still essential under nutrient-rich conditions. Different essentialities of different portions of the Pho regulatory pathway suggest that overexpression of the regulon is toxic and hint at a larger connection between phosphate regulation and cellular health. Lastly, various essentialities of different tRNAs hint at a more complex situation than would be expected for such a core process. These results expand upon research regarding cross-organism gene essentiality and further enrich the study of purple nonsulfur bacteria.ImportanceMicrobial genomic data are increasing at a tremendous rate, but physiological characterization of those data lags far behind. One mechanism of high-throughput physiological characterization is TnSeq, which uses high-volume transposon mutagenesis and high-throughput sequencing to identify all of the essential genes in a given organism's genome. Here TnSeq was used to identify essential genes in the metabolically versatile betaproteobacterium Rubrivivax gelatinosus The results presented here add to the growing TnSeq field and also reveal important aspects of R. gelatinosus physiology, which are applicable to researchers working on metabolically flexible organisms.
| S-EPMC4966436 | biostudies-literature
Project description:UnlabelledThe abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins.AvailabilityProtein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.
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Project description:BACKGROUND: Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases. RESULTS: In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates. CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.
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Project description:SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
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Project description:The 'Human Immunodeficiency Virus Type 1 (HIV-1), Human Interaction Database', available through the National Library of Medicine at http://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions, serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. Each HIV-1 human protein interaction can be retrieved without restriction by web-based downloads and ftp protocols and includes: Reference Sequence (RefSeq) protein accession numbers, National Center for Biotechnology Information Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. In addition to specific HIV-1 protein-human protein interactions, included are interaction effects upon HIV-1 replication resulting when individual human gene expression is blocked using siRNA. A total of 3142 human genes are described participating in 12,786 protein-protein interactions, along with 1316 replication interactions described for each of 1250 human genes identified using small interfering RNA (siRNA). Together the data identifies 4006 human genes involved in 14,102 interactions. With the inclusion of siRNA interactions we introduce a redesigned web interface to enhance viewing, filtering and downloading of the combined data set.
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Project description:Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein interaction (PPI) from the Human Protein Reference Database (HPRD). To determine key node of marbling, the degree and betweenness centrality (BC) were used. The hub nodes and biological pathways of our network are consistent with the previous reports about marbling traits, and also suggest unknown candidate genes associated with intramuscular fat. Five nodes were identified as hub genes, which was consistent with the network analysis using quantitative reverse-transcription PCR (qRT-PCR). Key nodes of the PPI network have positive roles (PPAR?, C/EBP?, and RUNX1T1) and negative roles (RXRA, CAMK2A) in the development of intramuscular fat by several adipogenesis-related pathways. This study provides genetic information for identifying candidate genes for the marbling trait in bovine.
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