Breaking Free from the Chains of Pathway Annotation:
            <i>de Novo</i>
            Pathway Discovery for the Analysis of Disease Processes

Lehne, Benjamin; Schlitt, Thomas

doi:10.2217/pgs.12.170

Cited by 6 publications

(7 citation statements)

References 100 publications

(79 reference statements)

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“…Such networks have been proposed as de novo pathways and partly remedy the limitations of KEGG pathway analysis [25]. Consequently, we mapped the liver fibrosis-relevant genes to a high-confidence human PPI network.…”

Section: Resultsmentioning

confidence: 99%

“…In literature related to liver fibrosis, the work of Affo et al [24] utilizes KEGG pathway analysis in identifying the role of focal adhesion and cytokine-cytokine receptor interaction pathways in alcoholic hepatitis. Although widely used, pathway analysis has some limitations [25]. Foremost, pathway analysis relies exclusively on experimentally confirmed, curated data.…”

Section: Introductionmentioning

confidence: 99%

“…For example, we found that only 5,870 of the ∼21,000 human genes mapped to the 229 KEGG pathways [26]. Moreover, the gene networks comprising the pathway maps are not mutually exclusive, and the same gene can occur in many pathways [25]. Integrated analyses of gene expression data with protein-protein interaction (PPI) networks enable us to partly overcome the limitations associated with pathway analysis.…”

Section: Introductionmentioning

confidence: 99%

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Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

et al. 2014

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Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

et al. 2014

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“…Thus, translating the analysis of human variants performed in-silico into the clinical setting is still one of the main challenges towards the goal of precision medicine. Several ways of addressing this challenge exist, such as predicting the phenotype[177] or response to drug treatment that will result from the variant. However, current techniques are constrained by the limited knowledge regarding genotypic and phenotypic relationships that can be used as inference.…”

Section: Final Remarksmentioning

confidence: 99%

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Peterson

Doughty

Kann

2013

Journal of Molecular Biology

121

122

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Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

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“…Networks are ideally suited to this purpose because biological function is hypothesized to arise from systems of molecular interactions [Emmert‐Streib and Glazko, ]. Networks that systematically describe these interactions can therefore group together functionally related genes beyond existing curated biological pathways [Lehne and Schlitt, ]. Finally, the protein products of genes causing phenotypically similar diseases are more likely to physically interact than those from non‐disease genes [Goh et al., ; Feldman et al., ].…”

Section: Introductionmentioning

confidence: 99%

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease

et al. 2015

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Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole‐exome sequencing generates a large number of candidate disease‐causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene‐ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow‐up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease‐causing genes than existing analysis methods. We also demonstrate a proof‐of‐principle application of the method to prioritize genes causing Adams‐Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/.

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Breaking Free from the Chains of Pathway Annotation: de Novo Pathway Discovery for the Analysis of Disease Processes

Cited by 6 publications

References 100 publications

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease

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