The STRING database
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14th International Conference on Intelligent Systems for Molecular Biology, Software demo, Fortaleza Conference Center, Fortaleza, Brazil, August 6-10, 2006
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The STRING database and related toolsLars Juhl Jensen The document discusses the STRING database and related tools for exploring protein-protein association networks, gene neighborhoods, phylogenetic profiles, and other computational predictions and experimental data. It notes that individual databases cover different species and formats, and have variable quality. STRING aims to integrate these resources using common identifiers, quality scores, and text mining while calibrating scores against experimental data and curated knowledge. Resources discussed include STRING for protein networks, STITCH for chemical networks, and COMPARTMENTS and TISSUES for subcellular localization and tissue expression data.
Using networks to derive function
Using networks to derive functionLars Juhl Jensen This document discusses using networks to derive biological function from genomic data. It mentions several types of data that can be used like gene expression, protein-protein interactions, genetic interactions, pathways, literature mining, and co-mentioning in text. It also notes challenges integrating these diverse data sources that have different formats, identifiers, quality, and are spread across many databases and genomes. Lastly, it recommends combining all available evidence to predict functional associations.
Network biology: Large-scale data and text mining
Network biology: Large-scale data and text miningLars Juhl Jensen This document discusses network biology and large-scale data and text mining. It describes how Lars Jensen uses computational predictions from over 1100 genomes along with experimental data and information extracted from text to build protein-protein association networks in STRING. These networks integrate known and predicted protein-protein interactions with functional associations, and are used to study biological systems at the network level.
Computational approaches to cell cycle analysis: Data and databases
Computational approaches to cell cycle analysis: Data and databasesLars Juhl Jensen The document discusses various databases that contain biological data including genes, proteins, genomes, protein domains, gene expression, interactions, pathways, and models. It mentions several specific databases such as GenBank, UniProt, PDB, SGD, TAIR, Ensembl, InParanoid, HomoloGene, COG, OrthoMCL, Entrez, eggNOG, TreeFam, CDD, Pfam, SMART, InterPro, SCOP, CATH, GEO, ArrayExpress, Cyclebase, BioGRID, DIP, IntAct, MINT, Prosite, ELM, Domino, Phospho.ELM, PhosPhAt, Phosida, CORUM,
Data integration and functional association networks
Data integration and functional association networksLars Juhl Jensen Exploring Modular Protein Architecture, European Molecular Biology Laboratory, Heidelberg, Germany, December 3-5, 2008
Advanced bioinformatics of proteomics datasets
Advanced bioinformatics of proteomics datasetsLars Juhl Jensen This document discusses advanced bioinformatics approaches for analyzing proteomics datasets, including using signaling networks, association networks, and text mining. It describes using machine learning to predict protein interactions and developing scoring schemes to integrate data from multiple sources. The document also covers using text mining approaches like named entity recognition and information extraction to analyze the large amount of proteomics information available in scientific literature.
Integration of diverse large-scale datasets
Integration of diverse large-scale datasetsLars Juhl Jensen The document discusses the integration of diverse large-scale datasets to build comprehensive protein-protein interaction networks. It describes challenges with data from different sources having different identifiers, evidence types and quality. It also discusses methods used by STRING and other databases to combine data from curated databases, literature mining, primary datasets and transfer of interactions based on orthology. Examples are given of cell cycle studies in yeast that have analyzed periodically expressed genes and protein interactions.
Large-scale integration of data and text
Large-scale integration of data and textLars Juhl Jensen This document discusses large-scale integration of biological data and text mining. It describes three main parts: association networks that connect entities based on "guilt by association", protein interaction networks built using data from STRING and 2000+ genomes, and using genomic context like gene fusion, gene neighborhood, and phylogenetic profiles. It then provides examples of using STRING to query protein networks and discusses challenges of text mining like the exponential growth of literature and limitations of current natural language processing. Finally, it describes the Jensen Lab's approach of integrating curated knowledge, experimental data, predictions, and data from databases like STRING, STITCH, PubChem, COMPARTMENTS, Gene Ontology, UniProtKB, and disease databases into a common framework with
Exploring proteins, chemicals and their interactions with STRING and STITCH
Exploring proteins, chemicals and their interactions with STRING and STITCHbiocs This document summarizes databases and computational methods for exploring protein and chemical interactions. It introduces STRING and STITCH, which integrate various sources of data to predict interactions. STRING contains information on protein-protein interactions for 373 genomes. STITCH contains data on interactions between proteins and chemicals, including drugs. The document provides an example of using NetworKIN to predict kinase-substrate relationships and discusses how these databases and methods can provide insights into interaction networks and biological functions.
STRING: Large-scale data and text mining
STRING: Large-scale data and text miningLars Juhl Jensen This document discusses large-scale data and text mining techniques used by STRING to build comprehensive protein association networks. STRING integrates information from genomic context, high-throughput experiments, co-expression and curated databases to assign a confidence score to each association. Natural language processing is applied to mine the scientific literature and extract entity and relation information from millions of articles and abstracts to expand the known protein association networks beyond curated knowledge. STRING is freely accessible online and allows users to perform queries and analyze networks for various organisms.
STRING: Prediction of protein networks through integration of diverse large-s...
STRING: Prediction of protein networks through integration of diverse large-s...Lars Juhl Jensen STRING integrates diverse large-scale data sets to predict protein networks. It provides a protein network for each species based on evidence from genomic neighborhood, gene fusions, co-expression, co-mentioning in literature, and phylogenetic profiles. Multiple types of evidence are calibrated against KEGG maps and combined to generate a consistent scoring scheme and predict functional associations between proteins across species.
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One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...Lars Juhl Jensen This document summarizes a Twitter thread discussing the uses of a dictionary-based named entity recognition tool called Tagger. Tagger can recognize genes, proteins, diseases and other biomedical entities. It is open source, runs quickly processing over 1000 abstracts per second, and achieves 70-80% recall and 80-90% precision. Tagger has been applied to tasks like identifying drug-disease associations, adverse drug events, and protein-protein interactions. It is available as a Docker container or web service.
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicineLars Juhl Jensen The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotationLars Juhl Jensen This document describes Extract 2.0, a text-mining tool that can assist with interactive annotation of documents. It uses dictionary-based tagging to identify relevant entities like genes and diseases. It achieves 70-80% recall and 80-90% precision on entity extraction and was evaluated in BioCreative challenges where it received positive feedback from curators. The tool is open source and available as a web service or Python wrapper.
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using CytoscapeLars Juhl Jensen This document discusses using Cytoscape, a network analysis tool, to import and visualize networks from STRING and STITCH databases. It provides three examples of networks created from literature and disease queries, demonstrating how to import networks and tables, apply node attributes and visual styles, perform enrichment analysis, and more.
STRING & STITCH : Network integration of heterogeneous data
STRING & STITCH : Network integration of heterogeneous dataLars Juhl Jensen The document discusses STRING and STITCH, two online databases that integrate data on protein-protein interactions, pathways, and functional associations from various sources. STRING collects data on over 9.6 million proteins and 430 thousand chemicals from sources like text mining, experimental assays, and co-expression analyses. It aims to provide a comprehensive global view of known and predicted protein associations. STITCH also integrates interaction data but focuses more on chemical-protein interactions. Both databases provide user-friendly web interfaces for browsing and visualizing interaction networks.
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured textLars Juhl Jensen 1) Lars Juhl Jensen discusses biomedical text mining and automatic processing of unstructured text such as patent literature, grant proposals, FDA product labels, and electronic medical records.
2) Named entity recognition is used to identify genes/proteins, chemical compounds, diseases, and other entities in text through comprehensive dictionaries and flexible matching rules that account for variations.
3) Relation extraction uses natural language processing techniques like part-of-speech tagging and sentence parsing along with manually crafted rules and machine learning to identify implicit relations between entities in text such as transcription factor targets, kinase substrates, and protein-protein interactions.
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...Lars Juhl Jensen The document summarizes the work of Lars Juhl Jensen and others on medical network analysis and linking diseases and genes through data and text mining of electronic health records. It discusses how they have used Danish national health registries containing data on over 6 million patients and 119 million diagnoses over 14 years to study disease trajectories and comorbidities. It also describes how they have developed methods to integrate data from various sources to generate networks linking diseases and genes.
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and CytoscapeLars Juhl Jensen This document provides an overview of STRING, a protein-protein association database, and Cytoscape, a network visualization tool. It describes how STRING contains functional associations between proteins derived from genomic context, co-expression and curated databases. Cytoscape can import STRING networks and external data to map onto nodes. It offers visualization of networks through layouts and attributes, and analysis through clustering, selection filters and enrichment. The document recommends using these tools together to explore protein association networks.
Cellular networks
Cellular networksLars Juhl Jensen This document discusses different approaches to visualizing cellular networks and the molecular interactions between proteins. It notes that there are many different types of data that could be shown, such as protein names, functions, localization, expression, modifications, and interaction types. However, it is impossible to show all this information at once. The document recommends using different visualizations like force-directed layouts to distribute proteins in 2D or lining up interactions in 1D. It acknowledges open challenges like showing time-course data and modification sites. In the end, the document thanks several researchers who have contributed to mapping and visualizing cellular networks.
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and textLars Juhl Jensen The document discusses various community resources and software tools for integrating large-scale data and text, including STRING for protein networks, STITCH for chemical networks, COMPARTMENTS for subcellular localization, TISSUES for tissue expression, and DISEASES for disease associations. It provides an overview of text mining techniques used to extract information from literature to build networks in these resources. The presenter demonstrates the Cytoscape App which can import and analyze networks from STRING, perform queries, and analyze subcellular localization, tissue expression, and disease enrichment.
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...Lars Juhl Jensen This document discusses statistical methods for analyzing high-throughput biomedical screens and common pitfalls. It introduces several statistical tests such as t-tests, ANOVA, Fisher's exact test, and the Mann-Whitney U test. It also discusses challenges like multiple testing, resampling techniques, and biases that can occur like studiedness bias and abundance bias in big data analyses. Controlling false discovery rates and considering effect sizes are recommended over solely relying on p-values to determine biological significance.
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous dataLars Juhl Jensen The document discusses the STRING database, which integrates heterogeneous biological data to generate association networks for proteins. It describes how STRING collects and connects curated knowledge, experimental data, and predicted interactions from genomic context, co-expression and text mining. The document also outlines exercises for users to explore protein-protein associations in STRING and related databases that integrate data on subcellular localization, tissue expression, and disease associations.
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognitionLars Juhl Jensen Tagger is a named entity recognition tool that can process over 1000 abstracts per second using a dictionary-based approach. It achieves 70-80% recall and 80-90% precision using comprehensive dictionaries, expansion rules, and a curated blacklist to identify entity types like genes, proteins, chemicals, and diseases. The tool has a C++ engine, is inherently thread-safe, and includes interactive annotation, Python wrappers, and a REST API.
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and textLars Juhl Jensen Lars Juhl Jensen leads a group that conducts large-scale integration of biological and medical data using proteomics, text mining, and medical data mining. The group develops protein interaction networks, disease networks, and association networks. They collaborate internationally on projects involving over 9.6 million proteins and 2000 genomes. The group works to integrate data from many sources in different formats to build comprehensive networks and knowledgebases, and also mines biomedical text to link genes and proteins with diseases.
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen This document discusses medical text mining and linking diseases, drugs, and adverse reactions. It describes using text mining on clinical narratives in Danish to recognize named entities like drugs and diseases, identify relationships between them like adverse drug reactions, and discover new ADRs. The goal is to generate structured data on topics like comorbidities, diagnosis trajectories, and reimbursement to supplement limited structured data and help busy doctors by analyzing large amounts of unstructured text.
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textLars Juhl Jensen The document discusses network biology and large-scale data integration. It describes protein-protein interaction networks like STRING that integrate data from curated knowledge, experiments, and predictions. It provides exercises to explore the human insulin receptor (INSR) in STRING, examining the types of evidence that support its interaction with IRS1. It also introduces other integrated networks like STITCH for chemicals and COMPARTMENTS for subcellular localization. Natural language processing techniques like named entity recognition, information extraction, and semantic tagging are used to integrate text data from the literature into these interaction networks.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using structured data from Danish central registries and unstructured data from hospital electronic health records. Named entity recognition is used to extract diseases, drugs, and adverse reactions from free text clinical notes written in Danish. Hand-crafted rules are developed to identify relationships between extracted entities like adverse drug reactions. This allows estimating frequencies of known adverse drug reactions and discovering new adverse drug reactions by analyzing diagnosis trajectories and medication information.
Cellular Network Biology
Cellular Network BiologyLars Juhl Jensen This document discusses cellular network biology and summarizes several key papers on topics like proteome analysis using mass spectrometry, integrating protein network and experimental data, challenges with different biological databases having varying formats and quality, and using natural language processing techniques like named entity recognition and relation extraction to analyze medical text for information like diagnosis trajectories and adverse drug reactions.
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and textLars Juhl Jensen This document discusses natural language processing (NLP) techniques for extracting information from biomedical literature and integrating it with network and interaction data. It describes how NLP is used to identify entities like genes and proteins, extract relationships between entities, and integrate this text-mined information with existing interaction networks from databases like STRING to expand knowledge of protein interactions, complexes, pathways and associations with diseases. The document provides examples of using NLP analysis on sentences and the STRING and Tissues databases to explore tissue specificity and disease relationships for insulin and the insulin receptor.
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The STRING database
- 1. The STRING database Lars Juhl Jensen EMBL Heidelberg
- 7. Ensembl
- 8. SWISS-PROT
- 16. gene fusion
- 23. sum of intergenic distances
- 25. many types of evidence
- 28. benchmarking
- 32. KEGG Kyoto Encyclopedia of Genes and Genomes
- 33. Reactome
- 34. MIPS Munich Information center for Protein Sequences
- 35. STKE Signal Transduction Knowledge Environment
- 37. many sources
- 38. parsers
- 39. co-expression
- 40. GEO Gene Expression Omnibus
- 41. SMD Stanford Microarray Database
- 43. BIND Biomolecular Interaction Network Database
- 44. MINT Molecular Interactions Database
- 45. GRID General Repository for Interaction Datasets
- 46. DIP Database of Interacting Proteins
- 47. HPRD Human Protein Reference Database
- 50. synonyms lists
- 51. M EDLINE
- 52. SGD Saccharomyces Genome Database
- 54. OMIM Online Mendelian Inheritance in Man
- 55. co-mentioning
- 56. NLP Natural Language Processing
- 57. Gene and protein names Cue words for entity recognition Verbs for relation extraction [ nxgene The GAL4 gene ] [ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ]
- 60. spread over many species
- 65. fuzzy orthology
- 66. ? Source species Target species
- 69. Acknowledgments The STRING team (EMBL) Christian von Mering Berend Snel Martijn Huynen Sean Hooper Samuel Chaffron Julien Lagarde Mathilde Foglierini Peer Bork Literature mining project (EML Research) Jasmin Saric Rossitza Ouzounova Isabel Rojas