The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest
The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest is a dataset published in Nucleic Acids Research (2022). On theSindex it has a DataRank of 9.4, placing it in the top 22.8% of the data-sharing corpus. It has been cited 8,502 times, with 197 citing works in its 1-hop citation network. Its calibrated FAIR score is 72/100.
Abstract
AbstractMuch of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein–protein interactions—both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.3
From this paper's citation signal
Citation Network Contribution
8.1
From 197 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- The Protein Data BankNucleic Acids Research200039,606 citationsDataRank 32.3Top 1%
- STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasetsNucleic Acids Research201819,062 citationsDataRank 13.8Top 14%
- NCBI GEO: archive for functional genomics data sets—updateNucleic Acids Research201210,702 citationsDataRank 20.2Top 5%
- The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement setsNucleic Acids Research20208,484 citationsDataRank 11.6Top 18%
- The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidencesNucleic Acids Research20216,690 citationsDataRank 12.1Top 17%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 14% comes from its base citations and 86% from the citation network (197 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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