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      Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update

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          Abstract

          In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges.

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          Most cited references48

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          Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations

          Evidence for the etiology of autism spectrum disorders (ASD) has consistently pointed to a strong genetic component complicated by substantial locus heterogeneity 1,2 . We sequenced the exomes of 20 sporadic cases of ASD and their parents, reasoning that these families would be enriched for de novo mutations of major effect. We identified 21 de novo mutations, of which 11 were protein-altering. Protein-altering mutations were significantly enriched for changes at highly conserved residues. We identified potentially causative de novo events in 4/20 probands, particularly among more severely affected individuals, in FOXP1, GRIN2B, SCN1A, and LAMC3. In the FOXP1 mutation carrier, we also observed a rare inherited CNTNAP2 mutation and provide functional support for a multihit model for disease risk 3 . Our results demonstrate that trio-based exome sequencing is a powerful approach for identifying novel candidate genes for ASD and suggest that de novo mutations may contribute substantially to the genetic risk for ASD.
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            Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

            Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.
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              Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference.

              The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell-context-specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high-throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities.
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                Author and article information

                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                31 October 2013
                2013
                : 2
                : 230
                Affiliations
                [1 ]Centre for High-Throughput Biology and Department of Psychiatry, University of British Columbia, Vancouver, V6T1Z4, Canada
                [2 ]Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY, 11797, USA
                [1 ]National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
                [1 ]Department of Molecular Medicine, Università degli studi di Padova, Padova, Italy
                Author notes

                PP and JG conceived and wrote the article.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Article
                10.12688/f1000research.2-230.v1
                3962002
                24715959
                78c7b904-c890-4619-bf2d-20bd521deb0c
                Copyright: © 2013 Pavlidis P and Gillis J

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

                History
                : 21 October 2013
                Funding
                Funded by: National Institutes of Health
                Award ID: GM076990
                Funded by: Michael Smith Foundation for Health Research
                Funded by: Canadian Institutes for Health
                PP was supported by NIH Grant GM076990 and salary awards from the Michael Smith Foundation for Health Research and the Canadian Institutes for Health. JG was supported by a grant from T. and V. Stanley.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Opinion Article
                Articles
                Bioinformatics
                Genomics

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