STRING 8--a global view on proteins and their functional interactions in 630 organisms

Jensen, Lars Juhl; Kuhn, Michael; Stark, Manuel; Chaffron, Samuel; Creevey, Christopher J.; Muller, Jean; Doerks, Tobias; Julien, Philippe; Röth, Alexander; Simonovic, Milan; Bork, Peer; Mering, Christian von

doi:10.1093/nar/gkn760

Cited by 2,220 publications

(2,163 citation statements)

References 33 publications

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“…Further, Gene Ontology (GO) enrichment analysis showed that the differentially expressed genes contained statistically significant enrichments of genes involved in glycolysis, cellular response to IL-4 stimulation and positive regulation of B-cell proliferation (Supplementary Table 6). To establish whether the genes distinguishing the two clusters act in a coordinated manner, we studied their interactions using the STRING database 41 . This yielded a densely connected network with three major hubs, which were highly enriched for glycolysis, translational elongation and T-cell activation, respectively (Supplementary Fig.…”

Section: Application Of Sclvm To Identify Cell Populations In Differementioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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Section: Application Of Sclvm To Identify Cell Populations In Differementioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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“…Reactome 23 and STRING (ref. 24) database analyses revealed that genes encoding core proteins of complexes involved in cell cycle progression, general transcription, messenger RNA processing and protein translation (for example, almost all core subunits of the ribosome, Table 1) were highly enriched in reduced-viability phenotypes in both cell lines. These genes therefore represent the molecular machineries that drive basic cellular proliferation and were excluded from further analysis.…”

Section: Genome-scale Synthetic Interaction Rnai Screen With Tp53mentioning

confidence: 99%

A systematic RNAi synthetic interaction screen reveals a link between p53 and snoRNP assembly

et al. 2011

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TP53(tumour protein 53) is one of the most frequently mutated genes in human cancer and its role during cellular transformation has been studied extensively. However, the homeostatic functions of p53 are less well understood. Here, we explore the molecular dependency network of TP53 through an RNAi-mediated synthetic interaction screen employing two HCT116 isogenic cell lines and a genome-scale endoribonuclease-prepared short interfering RNA library. We identify a variety of TP53 synthetic interactions unmasking the complex connections of p53 to cellular physiology and growth control. Molecular dissection of the TP53 synthetic interaction with UNRIP indicates an enhanced dependency of TP53-negative cells on small nucleolar ribonucleoprotein (snoRNP) assembly. This dependency is mediated by the snoRNP chaperone gene NOLC1 (also known as NOPP140 ), which we identify as a physiological p53 target gene. This unanticipated function of TP53 in snoRNP assembly highlights the potential of RNAi-mediated synthetic interaction screens to dissect molecular pathways of tumour suppressor genes.More than 30 years of intense research on the tumour suppressor gene TP53 has revealed its relevance in many aspects of tumour biology (reviewed in ref. 1). It is now well established that p53 (the protein product of TP53) activation leads to the execution of a complex genetic program that limits cellular proliferation and leads to senescence or apoptosis 2,3 . Several lines of evidence have pointed to an under-appreciated homeostasis function of p53 in normal, unstressed cells 4 . As a transcription factor, p53 can mediate the expression levels of its target genes, thereby adjusting cellular homeostasis of basic metabolism 5 , balance of reactive oxygen species 6,7 , stem cell identity and reprogramming 8 and animal reproduction 9 . These data establish an important role for p53 in cellular homeostasis and indicate that p53-loss-of-function cells become genetically rewired in a way that discriminates them from wild-type cells, a premise that could be explored for the identification of possible p53 genetic dependencies.Synthetic interaction screens have led the way in identifying new and unanticipated functional interactions between seemingly unrelated protein complexes in yeast 10,11 . In mammalian cells, RNA interference (RNAi)-based synthetic interaction screens are a promising approach to dissect genetic dependencies and the first studies on mammalian synthetic interactions of oncogenes have demonstrated the power of this approach 12,13 . Furthermore, synthetic lethal screens have identified genes that sensitize cells to different chemical drugs 14,15 . Several synthetic interactions with tumour suppressor genes have also been published 11,16 , but to our knowledge a genome-scale survey for synthetic interactions of a tumour suppressor gene has not been reported. Here, we present the results of a systematic investigation of TP53 genetic dependencies and characterize in molecular detail a homeostatic function of p53 in snoRNP biogen...

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“…As this process is very time-consuming however, there are databases that provide this cross-referenced information pre-computed. One such database is STRING (search tool for the retrieval of interacting genes/proteins; http://string.embl.de/; Jensen et al, 2009) which combines, scores and weights available information from multiple sources on protein-protein associations. This information is then further augmented with predicted interactions and the results of automatic literature-mining searches.…”

Section: Identifying Connections In the Systemmentioning

confidence: 99%

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Berry

Meade

Mullen

et al. 2011

Animal

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Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists -and they will also be discussed.

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STRING 8--a global view on proteins and their functional interactions in 630 organisms

Cited by 2,220 publications

References 33 publications

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

A systematic RNAi synthetic interaction screen reveals a link between p53 and snoRNP assembly

The integration of ‘omic’ disciplines and systems biology in cattle breeding

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