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Chicas A.,Cold Spring Harbor Laboratory | Wang X.,MOE Key Laboratory of Bioinformatics | Zhang C.,Cold Spring Harbor Laboratory | McCurrach M.,Cold Spring Harbor Laboratory | And 7 more authors.
Cancer Cell | Year: 2010

The RB protein family (RB, p107, and p130) has overlapping and compensatory functions in cell-cycle control. However, cancer-associated mutations are almost exclusively found in RB, implying that RB has a nonredundant role in tumor suppression. We demonstrate that RB preferentially associates with E2F target genes involved in DNA replication and is uniquely required to repress these genes during senescence but not other growth states. Consequently, RB loss leads to inappropriate DNA synthesis following a senescence trigger and, together with disruption of a p21-mediated cell-cycle checkpoint, enables extensive proliferation and rampant genomic instability. Our results identify a nonredundant RB effector function that may contribute to tumor suppression and reveal how loss of RB and p53 cooperate to bypass senescence. © 2010 Elsevier Inc. All rights reserved. Source


Chen Y.,MOE Key Laboratory of Bioinformatics | Wu X.,Massachusetts Institute of Technology | Jiang R.,MOE Key Laboratory of Bioinformatics
BMC Medical Genomics | Year: 2013

Background: The identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods. Methods. With this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown. Results: We conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes. Conclusion: The proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases. © 2013 Chen et al.; licensee BioMed Central Ltd. Source


Jiang R.,MOE Key Laboratory of Bioinformatics | Wu M.,MOE Key Laboratory of Bioinformatics | Li L.,MOE Key Laboratory of Bioinformatics
BMC Genomics | Year: 2015

Background: Pinpointing genes involved in inherited human diseases remains a great challenge in the post-genomics era. Although approaches have been proposed either based on the guilt-by-association principle or making use of disease phenotype similarities, the low coverage of both diseases and genes in existing methods has been preventing the scan of causative genes for a significant proportion of diseases at the whole-genome level.Results: To overcome this limitation, we proposed a rigorous statistical method called pgFusion to prioritize candidate genes by integrating one type of disease phenotype similarity derived from the Unified Medical Language System (UMLS) and seven types of gene functional similarities calculated from gene expression, gene ontology, pathway membership, protein sequence, protein domain, protein-protein interaction and regulation pattern, respectively. Our method covered a total of 7,719 diseases and 20,327 genes, achieving the highest coverage thus far for both diseases and genes. We performed leave-one-out cross-validation experiments to demonstrate the superior performance of our method and applied it to a real exome sequencing dataset of epileptic encephalopathies, showing the capability of this approach in finding causative genes for complex diseases. We further provided the standalone software and online services of pgFusion at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgfusion.Conclusions: pgFusion not only provided an effective way for prioritizing candidate genes, but also demonstrated feasible solutions to two fundamental questions in the analysis of big genomic data: the comparability of heterogeneous data and the integration of multiple types of data. Applications of this method in exome or whole genome sequencing studies would accelerate the finding of causative genes for human diseases. Other research fields in genomics could also benefit from the incorporation of our data fusion methodology. © 2015 Jiang et al.; licensee BioMed Central Ltd. Source

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