Large-scale investigation of the reasons why potentially important genes are ignored

Stoeger, Thomas; Gerlach, Martin; Morimoto, Richard I.; Amaral, Luı́s A. Nunes

doi:10.1371/journal.pbio.2006643

Cited by 233 publications

(264 citation statements)

References 70 publications

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“…Considering the PPI network, the higher degree centrality of genes differentially expressed with age in most tissues is not especially surprising. Several of the identified genes are well studied and PPI data favours proteins of high abundance (von Mering et al, 2002) and with high publication coverage (Stoeger et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

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Understanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing

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Section: Discussionmentioning

confidence: 99%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

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“…Significant efforts in biological research have been devoted to defining the molecular and physiological functions of genes. However, many genes are still not well annotated and even remain uncharacterized (Edwards et al 2011;Dolgin 2017;Stoeger et al 2018). Here we developed an approach, termed G-MAD, to facilitate the identification of novel gene functions and to establish robust connections between genes and modules.…”

Section: Discussionmentioning

confidence: 99%

“…This is also true for gene annotations retrieved from other sources, such as GeneRIF (Gene Reference Into Function) (Mitchell et al 2003 ). The phenomenon that many genes are ignored in biological research has been pointed out before (Edwards et al 2011;Pandey et al 2014;Stoeger et al 2018). Several possible reasons for this bias, such as prior knowledge, publication bias, and priorities of funding support have been raised (Edwards et al 2011;Greene and Troyanskaya 2012;Stoeger et al 2018).…”

Section: Current Status Of Gene Annotationsmentioning

confidence: 99%

“…The identification of gene function and the integrated understanding of their roles in physiology are core aims of many biological and biomedical research projects -an effort that is still far from being complete (Edwards et al 2011;Pandey et al 2014;Dolgin 2017;Stoeger et al 2018). Traditionally, gene function has been elucidated through experimental approaches, including the evaluation of the phenotypic consequences of gain-or loss-of-function (G/LOF) mutations (Austin et al 2004;Dickinson et al 2016), or by genetic linkage or association studies (Williams and Auwerx 2015).…”

Section: Introductionmentioning

confidence: 99%

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Identifying gene function and module connections by the integration of multi-species expression compendia

Rukina

David

et al. 2019

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The functions of many eukaryotic genes are still poorly understood. We developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multi-species expression compendia-1,700 datasets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast-collected in this study. Unlike most existing bioinformatics tools, GeneBridge exploits both positive and negative gene/module-module associations. We constructed association networks, such as those bridging mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available at systems-genetics.org, to facilitate the identification of connections linking genes, modules, phenotypes, and diseases.

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“…This approach, coupled with semantic similarity methods for example, can 17 be used to compare diseases [5]. One difficulty in using solely curated or semantic data 18 sources, is the tendency for curatorial processes to produce annotations that are biased 19 toward well-studied species and processes, resulting in an uneven distribution of 20 annotations [6,7]. Furthermore, gene annotations are affected by the idiosyncrasies of 21 experimental systems and assays [8] which may impact downstream analysis.…”

Section: Introductionmentioning

confidence: 99%

Finding human gene-disease associations using a Network Enhanced Similarity Search (NESS) of multi-species heterogeneous functional genomics data

Reynolds

Bubier

Langston

et al. 2020

Preprint

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Disease diagnosis and treatment is challenging in part due to the misalignment of diagnostic categories with the underlying biology of disease. The evaluation of large-scale genomic experimental datasets is a compelling approach to refining the classification of biological concepts, such as disease. Well-established approaches, some of which rely on information theory or network analysis, quantitatively assess relationships among biological entities using gene annotations, structured vocabularies, and curated data sources. However, the gene annotations used in these evaluations are often sparse, potentially biased due to uneven study and representation in the literature, and constrained to the single species from which they were derived. In order to overcome these deficiencies inherent in the structure and sparsity of these annotated datasets, we developed a novel Network Enhanced Similarity Search (NESS) tool which takes advantage of multi-species networks of heterogeneous data to bridge sparsely populated datasets.NESS employs a random walk with restart algorithm across harmonized multi-species data, effectively compensating for sparsely populated and noisy genomic studies. We further demonstrate that it is highly resistant to spurious or sparse datasets and generates significantly better recapitulation of ground truth biological pathways than other similarity metrics alone. Furthermore, since NESS has been deployed as an embedded tool in the GeneWeaver environment, it can rapidly take advantage of curated multi-species networks to provide informative assertions of relatedness of any pair of biological entities or concepts, e.g., gene-gene, gene-disease, or phenotype-disease associations. NESS ultimately enables multi-species analysis applications to leverage model organism data to overcome the challenge of data sparsity in the study of human disease. Availability and Implementation: Implementation available at https://geneweaver.org/ness. Source code freely available at https://github.com/treynr/ness. Author summaryFinding consensus among large-scale genomic datasets is an ongoing challenge in the biomedical sciences. Harmonizing and analyzing such data is important because it November 25, 2019 1/19 allows researchers to mitigate the idiosyncrasies of experimental systems, alleviate study biases, and augment sparse datasets. Additionally, it allows researchers to utilize animal model studies and cross-species experiments to better understand biological function in health and disease. Here we provide a tool for integrating and analyzing heterogeneous functional genomics data using a graph-based model. We show how this type of analysis can be used to identify similar relationships among biological entities such as genes, processes, and disease through shared genomic associations. Our results indicate this approach is effective at reducing biases caused by sparse and noisy datasets. We show how this type of analysis can be used to aid the classification gene function and prioritization of genes involved in substa...

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Large-scale investigation of the reasons why potentially important genes are ignored

Cited by 233 publications

References 70 publications

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Identifying gene function and module connections by the integration of multi-species expression compendia

Finding human gene-disease associations using a Network Enhanced Similarity Search (NESS) of multi-species heterogeneous functional genomics data

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