Data integration and functional association networks

Dec 5, 2008Download as ppt, pdf1 like373 views

Lars Juhl Jensen

Exploring Modular Protein Architecture, European Molecular Biology Laboratory, Heidelberg, Germany, December 3-5, 2008

Jensen, Kuhn et al., Nucleic Acids Research , 2009

Frishman et al., Modern Genome Annotation , 2009

Korbel et al., Nature Biotechnology , 2004

BIND Biomolecular Interaction Network Database

BioGRID General Repository for Interaction Datasets

MIPS Munich Information center for Protein Sequences

Letunic & Bork, Trends in Biochemical Sciences , 2008

KEGG Kyoto Encyclopedia of Genes and Genomes

PID NCI-Nature Pathway Interaction Database

OMIM Online Mendelian Inheritance in Man

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 ]

von Mering et al., Nucleic Acids Research , 2005

Linding, Jensen, Ostheimer et al., Cell , 2007

Acknowledgments NetworKIN.info Rune Linding Gerard Ostheimer Francesca Diella Karen Colwill Jing Jin Pavel Metalnikov Vivian Nguyen Adrian Pasculescu Jin Gyoon Park Leona D. Samson Rob Russell Peer Bork Michael Yaffe Tony Pawson STRING.embl.de Michael Kuhn Manuel Stark Samuel Chaffron Chris Creevey Jean Muller Tobias Doerks Philippe Julien Alexander Roth Milan Simonovic Peer Bork Christian von Mering STITCH.embl.de Michael Kuhn Christian von Mering Monica Campillos Peer Bork eggNOG.embl.de Philippe Julien Michael Kuhn Christian von Mering Jean Muller Tobias Doerks Peer Bork Reflect.ws Sean O’Donoghue Evangelos Pafilis Heiko Horn Michael Kuhn Peer Bork Reinhardt Schneider

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.

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Data integration - Integration of functional associations using STRINGLars Juhl Jensen

The STITCH and Reflect web resourcesLars Juhl Jensen

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This document discusses three topics: proteome analysis using mass spectrometry to identify proteins and modifications, network biology to build association networks between proteins, and text mining to extract biological information from literature to integrate diverse data sources and build networks. Mass spectrometry is used to analyze proteomes at large scale but has missing values, while networks identify enriched functions and show evolutionary conservation. Text mining extracts data from over 10,000 papers to integrate into networks due to the large number of databases with different formats. It uses natural language processing and co-mentioning to capture relationships beyond proteins from literature and other sources.

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This document discusses how gene association networks are created by integrating large amounts of genomic data and text from many databases. Researchers develop parsers and mapping files to combine information about genes from various sources, which may have different formats and identifiers. They also use text mining to extract gene and protein associations from literature. The resulting association networks provide a comprehensive view of functional relationships between genes and are made available through online resources like STRING-DB.

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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.

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This document discusses protein networks and how they can be constructed from data and text mining. It describes challenges like different data sources using different formats and identifiers and issues with data quality. It also outlines techniques used to parse the data, map identifiers, assign quality scores, and implicitly weight evidence by quality to build a comprehensive protein interaction network across all available sources. The resulting database is made freely available online as a web resource, downloadable files, and via an API and apps to facilitate its use.

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The document discusses the STRING database, which integrates data from diverse sources to predict protein-protein interactions and functional associations. It summarizes different lines of evidence used by STRING, including genomic context, co-expression, co-mentioning in articles, and transfer of functional annotations between orthologs. The document also briefly outlines how STRING scores and benchmarks different predictive methods and defines functional modules to model biological systems.

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STRING integrates diverse evidence about functional interactions between proteins from hundreds of proteomes. It combines data from genomic context methods, curated databases, experiments, and textmining to generate a global network of protein interactions. The different evidence sources have issues like inconsistent identifiers, variable quality, and coverage of different species that STRING addresses through parsers, orthology transfer, and quality scores to generate a single confidence score for each interaction.

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Gene association networks - Large-scale integration of data and textLars Juhl Jensen

This document discusses how gene association networks integrate large datasets and text to link genes based on various types of evidence from experimental data, curated knowledge, co-expression, and physical interactions. The associations are compiled into a comprehensive resource, STRING, which combines all evidence using quality scores and cross-species transfer to connect over 9.6 million genes into a large-scale network. The network is accessible online through the STRING website and Cytoscape app and provides a global view of functional gene associations.

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The document discusses methods for integrating different types of biological data to build networks that model signal transduction pathways. It describes using protein sequence motifs to predict kinase-substrate relationships, and combining this with protein interaction and expression data to provide context. Validation studies on ATM and Cdk1 signaling pathways showed this approach could accurately predict phosphorylation sites and the kinases that target them. Future work involves improving scoring methods and expanding to other types of post-translational modifications and model organisms.

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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.

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This document discusses gene association networks and large-scale data and text integration. It describes how STRING generates association networks from genomic context, gene fusion, coexpression, and curated knowledge from databases. Text mining is used to extract additional associations from the scientific literature, as natural language processing techniques like named entity recognition, information extraction, and semantic tagging are applied to extract gene and protein relationships from text. The extracted information is integrated with experimental interaction data to build comprehensive gene association networks.

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This document discusses network biology and text mining of large datasets to analyze protein and medical networks. It describes using techniques like named entity recognition, information extraction, and natural language processing on text corpora with millions of abstracts and articles to identify relationships between genes, proteins, and medical entities. The text also discusses using these methods to analyze protein interaction and medical diagnosis trajectory data to gain biological and medical insights.

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The document discusses network biology and integration of large-scale data and text to build interaction networks. It introduces the STRING database, which contains over 9.6 million proteins and integrates interaction data from curated databases, experiments, textmining, and predictive methods. The document uses human insulin receptor (INSR) as an example to demonstrate searching and analyzing the STRING network, showing evidence from different data sources for its interaction with IRS1. It also introduces other integrated networks in the STRING group including STITCH, COMPARTMENTS, TISSUES and DISEASES.

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This document discusses building protein networks through data and text mining. It describes integrating data from many databases on protein interactions and functional associations, which are in various formats and identifiers. Named entity recognition and co-mentioning are used to extract protein names and their relationships from text. The integrated data is then visualized in networks and databases like STRING provide this network data along with search and analysis tools through a web resource, files, and APIs.

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This document summarizes the STRING protein association database and network analysis tool. It integrates data from genomic context, gene fusions, co-expression and experimental interactions for over 9.6 million proteins. The data comes from various sources and is standardized and scored. Text mining is used to extract protein associations from over 10,000 PubMed abstracts. The network data can be accessed through the STRING website or downloaded for analysis in Cytoscape or R/Bioconductor. Users can perform protein, disease or PubMed queries.

A linear motif atlas for phosphorylation-dependent signalingLars Juhl Jensen

This document summarizes a number of resources for studying phosphorylation-dependent signaling networks, including databases of phosphorylation sites, sequence motifs, kinase-specific motifs, and tools for analyzing phosphorylation data and networks. It describes databases like NetworKIN that integrate phosphorylation site data, sequence motifs, and protein interaction networks to predict kinase-substrate relationships. Other resources mentioned include NetPhorest, which predicts kinase-specific phosphorylation motifs from in vitro data, and Reflect, an online tool for augmented browsing of phosphorylation and protein interaction data.

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The document discusses using drug side effects to identify new drug targets. It describes how analyzing the similarity between side effect profiles of different drugs can reveal shared targets, even for drugs that are chemically dissimilar. The author and others developed databases of drug side effect information from package inserts and text mining. They used this information to build a drug-drug network and test predictions, finding binding or activity for the majority of drug pairs examined. Future work involves better linking side effects to specific targets and direct target prediction.

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Professor Damon Ridley was our special guest speaker for this webinar. Damon was Professor of Chemistry at the University of Sydney until 2002 when he left to become Head of the Chemistry Department at Silverbrook Research – which then was Australia’s largest privately owned research organization. He has published over 150 scientific papers and is an inventor named in over 50 patents granted by the US Patent Office. However, he also is very well known internationally for his work and publications in scientific information retrieval. In this webinar Damon shared his years of experience with us and focused in particular on searching for substances in Reaxys.

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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...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 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 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.

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 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 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 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 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.

Biomarker bioinformatics: Network-based candidate prioritizationLars Juhl Jensen

The document discusses three parts of biomarker bioinformatics: data integration from multiple databases, text mining of scientific literature, and using that integrated data to prioritize biomarker candidates. It describes combining data on 9.6 million proteins from curated databases, using text mining to extract named entities from over 10,000 papers, and then using network and heat diffusion approaches to rank candidates based on evidence in the integrated data. The goal is to help identify new biomarker candidates from large amounts of biological data.

The Art of Counting: Scoring and ranking co-occurrences in literatureLars Juhl Jensen

The document discusses methods for scoring and ranking co-occurrences of entities like diseases and genes in literature. It describes counting co-occurrences within different text levels like documents, paragraphs and sentences, and using techniques like z-score transformations and weighted combinations that can rank entities for a given query without changing the overall ranking. The methods have been implemented in web tools that can return results for queries within seconds using preprocessed named entity recognition results stored in a relational database.

Text-mining-based retrieval of protein networksLars Juhl Jensen

This document describes a method for using text mining of biomedical literature to retrieve protein networks. Key aspects include using text mining and named entity recognition on sets of abstracts from PubMed queries to identify proteins of interest and their relationships, then constructing a protein interaction network. This network can then be explored and visualized using the Cytoscape App integration of the text mining approach within the STRING database framework.

One tagger, many uses: Illustrating the power of dictionary-based named entit...Lars Juhl Jensen

One tagger, many uses: Simple text-mining strategies for biomedicineLars Juhl Jensen

Extract 2.0: Text-mining-assisted interactive annotationLars Juhl Jensen

Network visualization: A crash course on using CytoscapeLars Juhl Jensen

Biomedical text mining: Automatic processing of unstructured textLars Juhl Jensen

Medical network analysis: Linking diseases and genes through data and text mi...Lars Juhl Jensen

Network Biology: A crash course on STRING and CytoscapeLars Juhl Jensen

Cellular networksLars Juhl Jensen

Cellular Network Biology: Large-scale integration of data and textLars Juhl Jensen

Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...Lars Juhl Jensen

STRING & related databases: Large-scale integration of heterogeneous dataLars Juhl Jensen

Tagger: Rapid dictionary-based named entity recognitionLars Juhl Jensen

Medical text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen

Network biology: Large-scale integration of data and textLars Juhl Jensen

Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen

Cellular Network BiologyLars Juhl Jensen

Network biology: Large-scale integration of data and textLars Juhl Jensen

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The Art of Counting: Scoring and ranking co-occurrences in literatureLars Juhl Jensen

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Data integration and functional association networks

1. Lars Juhl Jensen Data integration and functional association networks

2. Lars Juhl Jensen Data integration and functional association networks

3. if this is your plan

6. STRING

7. Jensen, Kuhn et al., Nucleic Acids Research , 2009

8. data integration

9. functional associations

10. Frishman et al., Modern Genome Annotation , 2009

11. the basis

12. 630 genomes

13. model organism databases

14. Ensembl

15. RefSeq

16. genomic context methods

17. gene fusion

18. Korbel et al., Nature Biotechnology , 2004

19. conserved neighborhood

20. Korbel et al., Nature Biotechnology , 2004

21. phylogenetic profiles

22. Korbel et al., Nature Biotechnology , 2004

23. primary experimental data

24. gene coexpression

25.

26. GEO Gene Expression Omnibus

27. protein interactions

28. Jensen & Bork, Science , 2008

29. BIND Biomolecular Interaction Network Database

30. BioGRID General Repository for Interaction Datasets

31. DIP Database of Interacting Proteins

32. IntAct

33. MINT Molecular Interactions Database

34. HPRD Human Protein Reference Database

35. PDB Protein Data Bank

36. curated knowledge

37. complexes

38. MIPS Munich Information center for Protein Sequences

39. Gene Ontology

40. pathways

41. Letunic & Bork, Trends in Biochemical Sciences , 2008

42. KEGG Kyoto Encyclopedia of Genes and Genomes

43. MetaCyc

44. Reactome

45. PID NCI-Nature Pathway Interaction Database

46. literature mining

47. M EDLINE

48. SGD Saccharomyces Genome Database

49. The Interactive Fly

50. OMIM Online Mendelian Inheritance in Man

51. thesaurus

52. co-mentioning

53.

54. NLP Natural Language Processing

55. 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 ]

56. too easy …

57. … to be true

58. many data types

59. not comparable

60. different error rates

61. many sources

62. different file formats

63. different gene identifiers

64. redundancy

65. spread over 630 genomes

66. raw quality scores

67. reproducibility

68. von Mering et al., Nucleic Acids Research , 2005

69. intergenic distances

70. Korbel et al., Nature Biotechnology , 2004

71. benchmarking

72. calibrate vs. gold standard

73. von Mering et al., Nucleic Acids Research , 2005

74. raw quality scores

75. probabilistic scores

76. transfer by orthology

77. von Mering et al., Nucleic Acids Research , 2005

78. two modes

79. COG mode

80. von Mering et al., Nucleic Acids Research , 2005

81. protein mode

82. von Mering et al., Nucleic Acids Research , 2005

83. combine all evidence

84. visualize

85. Frishman et al., Modern Genome Annotation , 2009

86. related resources

87. STITCH

88.

89. protein–chemical network

90.

91. Reflect

92.

93. eggNOG

94. orthologous groups

95.

96. NetworKIN

97.

98. Linding, Jensen, Ostheimer et al., Cell , 2007

99. Acknowledgments NetworKIN.info Rune Linding Gerard Ostheimer Francesca Diella Karen Colwill Jing Jin Pavel Metalnikov Vivian Nguyen Adrian Pasculescu Jin Gyoon Park Leona D. Samson Rob Russell Peer Bork Michael Yaffe Tony Pawson STRING.embl.de Michael Kuhn Manuel Stark Samuel Chaffron Chris Creevey Jean Muller Tobias Doerks Philippe Julien Alexander Roth Milan Simonovic Peer Bork Christian von Mering STITCH.embl.de Michael Kuhn Christian von Mering Monica Campillos Peer Bork eggNOG.embl.de Philippe Julien Michael Kuhn Christian von Mering Jean Muller Tobias Doerks Peer Bork Reflect.ws Sean O’Donoghue Evangelos Pafilis Heiko Horn Michael Kuhn Peer Bork Reinhardt Schneider

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