A Probabilistic Functional Network of Yeast Genes

Lee, Insuk; Date, Shailesh V.; Adai, Alex; Marcotte, Edward M.

doi:10.1126/science.1099511

Cited by 617 publications

(665 citation statements)

References 32 publications

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“…This reveals a considerable complementarity between the different methods: if methods are applied individually, some gene functions may be predicted highly accurately while the others not at all. A combination of genome sequence-based predictors is able to reach across many different GO functions, consistent with the success of past approaches that integrate across large-scale experimental data sources (Troyanskaya et al, 2003;Lee et al, 2004;Lanckriet et al, 2004;von Mering et al, 2005;Hu et al, 2009;Lee et al, 2010).…”

Section: Extensive Complementarity Between Afp Methodsmentioning

confidence: 65%

“…This was made evident in the analyses of gene/protein functional association networks, constructed using various sources of large-scale data. Integrating the individual networks resulted in gene modules that were more functionally consistent (Lee et al, 2004;von Mering et al, 2005) and could thus more accurately predict gene function (Troyanskaya et al, 2003;Hu et al, 2009) or phenotypic effects of gene perturbation (Lee et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Extensive complementarity between gene function prediction methods

2016

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Motivation: The number of sequenced genomes rises steadily, but we still lack the knowledge about the biological roles of many genes. Automatic function prediction (AFP) is thus a necessity. We hypothesize that AFP approaches which draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Results: Our pipeline amalgamates 5,133,543 genes from 2,071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1,227 Gene Ontology terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to simply rely on a single mostconfident prediction per gene/function, instead of enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known E. coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods, or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the CAFA2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

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Section: Extensive Complementarity Between Afp Methodsmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Extensive complementarity between gene function prediction methods

2016

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“…A weighted average of three independent replicates in each group (i.e. with and without TF) were analyzed, incorporating or not the information provided by a probabilistic functional gene network obtained from an independent study (Lee et al, 2004). …”

Section: Biologically Informed Microarray Experimentsmentioning

confidence: 99%

Integrating biological information into the statistical analysis and design of microarray experiments

Rosa

Vázquez

2010

Animal

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Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.

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“…4 Feed-forward loops (FFL), bi-parallel and bi-fan motifs are overrepresented (figure 2a). In addition, our analysis reveals a previously uncharacterized 4-FFL motif.…”

Section: P 10 (28)mentioning

confidence: 99%

“…This data is often analyzed by clustering over different experiments of wholegenome expression profiles, and that technique has provided important insights into gene function [2]. However, clustering alone cannot resolve gene interactions, and progress in network identification algorithms has revealed aspects of the static wiring of gene networks [3][4][5][6][7][8][9][10][11]. A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast.…”

Section: Introductionmentioning

confidence: 99%