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Journal ArticleDOI

NCBI GEO: archive for functional genomics data sets—update

TL;DR: The Gene Expression Omnibus is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community and supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable.
Abstract: The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
Citations
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Journal ArticleDOI
TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

10,584 citations


Cites methods from "NCBI GEO: archive for functional ge..."

  • ...In the case of transcript data, STRING re-processes and maps the large number of experiments stored in the NCBI Gene Expression Omnibus (46), followed by normalization, redundancy reduction and Pearson correlation (described in (29))....

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Journal ArticleDOI
TL;DR: H hierarchical and self-consistent orthology annotations are introduced for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution in the STRING database.
Abstract: The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

8,224 citations


Cites methods from "NCBI GEO: archive for functional ge..."

  • ...As of March 2014, GEO consisted of more than 12 000 different platforms (GPL), 45 000 experiments (GSE) and over 1 million matrices (GSM)....

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  • ...The improvements can be attributed to the increased size of the GEO repository (experiments added since 2011) and to changes in our pipeline, namely: (i) the additional step to prune highly correlated samples using the Hobohm-2 algorithm and (ii) several minor improvements and bug fixes....

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  • ...The co-expression scores in STRING v10 are computed using a revised and improved pipeline (Figure 2), making use of all microarray gene expression experiments deposited in NCBI Gene Expression Omnibus (NCBI GEO) (37)....

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Journal ArticleDOI
TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

6,201 citations


Cites methods from "NCBI GEO: archive for functional ge..."

  • ...The new gene set libraries that were added include differentially expressed genes after drug, gene, disease and pathogen perturbations extracted from the national center for biotechnology information (NCBI) gene expression omnibus (GEO) through a crowdsourcing project....

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  • ...To extract gene sets from gene expression data deposited in the GEO (29), we established a crowdsourcing microtask project that asks participants to extract gene sets from GEO for the following categories: (1) single-gene perturbations in mammalian cells; (2) comparison of diseased versus normal tissues; (3) single-drug perturbations in mammalian cells; (4) perturbations applied to MCF7 cells; (5) comparison between young and old mammalian tissues; (6) endogenous ligand perturbations of mammalian cells; and (7) comparison of before and after pathogen infection of human cells....

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  • ...Participants of the microtasks were recruited via two Coursera massive online open courses (MOOCs) and worked voluntarily on finding relevant studies from the GEO database....

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  • ...Participants were also instructed to use the browser extension GEO2Enrichr (30) to extract differentially expressed gene sets from GEO....

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  • ...This peak is likely an artifact from submissions that arrive from the tool GEO2Enrichr, which has a default setting of posting the top 500 genes separated into up-regulated or down-regulated genes from signatures processed from GEO....

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Journal ArticleDOI
TL;DR: An update of the miRBase database is described, including the collation and use of deep sequencing data sets to assign levels of confidence to miR base entries, and a high confidence subset of miR Base entries are provided, based on the pattern of mapped reads.
Abstract: We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

4,705 citations


Cites background from "NCBI GEO: archive for functional ge..."

  • ...Large numbers of deep sequencing data sets are publicly available in databases such as the Gene Expression Omnibus (15) and the Short Read Archive (16), and these data sets allow us to assess post hoc the confidence in any given microRNA annotation....

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  • ...Large numbers of deep sequencing data sets are publicly available in databases such as the Gene Expression Omnibus (15) and the Short Read Archive (16), and these data sets allow us to assess post hoc the confidence in any given microRNA annotation....

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Journal ArticleDOI
Jun-Hao Li1, Shun Liu1, Hui Zhou1, Liang-Hu Qu1, Jian-Hua Yang1 
TL;DR: This study developed starBase v2.0, which has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and mi RNA-lncRNA interaction networks to date, and developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from themiRNA-mediated regulatory networks.
Abstract: Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

3,597 citations


Cites methods from "NCBI GEO: archive for functional ge..."

  • ...HITS-CLIP, PAR-CLIP, iCLIP and CLASH data were retrieved from the Gene Expression Omnibus (22), the supplementary data of original references or directly from authors on request (Supplementary Table S1)....

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  • ...Integration of Ago and other RBP binding sites from published CLIP data HITS-CLIP, PAR-CLIP, iCLIP and CLASH data were retrieved from the Gene Expression Omnibus (22), the supplementary data of original references or directly from authors on request (Supplementary Table S1)....

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  • ...In addition, we intend to integrate the cancer genomics data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) into starBase to improve our understanding of miRNAmediated regulatory networks in developmental, physiological and pathological processes....

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References
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Journal ArticleDOI
06 Sep 2012-Nature
TL;DR: The Encyclopedia of DNA Elements project provides new insights into the organization and regulation of the authors' genes and genome, and is an expansive resource of functional annotations for biomedical research.
Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

13,548 citations

Journal ArticleDOI
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Abstract: The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

12,142 citations


"NCBI GEO: archive for functional ge..." refers methods in this paper

  • ...The GEO2R back end uses established Bioconductor (13) R packages to transform and analyse GEO data and presents results as a table of genes ordered by significance and that can be visualized with GEO Profile graphics....

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Journal ArticleDOI
TL;DR: The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
Abstract: The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

11,864 citations

Journal ArticleDOI
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

10,968 citations


"NCBI GEO: archive for functional ge..." refers background in this paper

  • ...The Gene Expression Omnibus (GEO) repository (1) archives and freely distributes microarray, next-generation sequencing (NGS) and other forms of high-throughput functional genomic data....

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Journal Article
01 Jan 2012-Nature
TL;DR: The Encyclopedia of DNA Elements project provides new insights into the organization and regulation of the authors' genes and genome, and is an expansive resource of functional annotations for biomedical research.
Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

8,106 citations

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