Advanced bioinformatics methods for proteomics

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The document summarizes advanced bioinformatics methods for proteomics, specifically focusing on three parts: signaling networks, association networks, and text mining. It discusses using phosphoproteomics to study signaling networks and unknown kinase-substrate relationships. It also describes the STRING database for studying association networks across more than 1100 genomes using various computational predictions and experimental data. Finally, it outlines using text mining approaches like named entity recognition and information extraction to analyze large amounts of biomedical literature and formalize facts.

Advanced bioinformatics
methods for proteomics

Lars Juhl Jensen

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

Miller, Jensen et al., Science Signaling, 2008

Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

Korbel et al., Nature Biotechnology, 2004

Beyer et al., Nature Reviews Genetics, 2007

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
O’Donoghue et al., Journal of Web Semantics, 2010

Acknowledgments
NetPhorest NetworKIN STRING Reflect
Rune Linding Rune Linding Christian von Mering Sune Frankild
Martin Lee Miller Heiko Horn Damian Szklarczyk Heiko Horn
Erwin Schoof Gerard Ostheimer Michael Kuhn Evangelos Pafilis
Francesca Diella Martin Lee Miller Manuel Stark Michael Kuhn
Claus Jørgensen Francesca Diella Samuel Chaffron Nigel Brown
Michele Tinti Karen Colwill Chris Creevey Reinhardt
Lei Li Jing Jin Jean Muller Schneider
Marilyn Hsiung Pavel Metalnikov Tobias Doerks Sean O’Donoghue
Sirlester A. Parker Vivian Nguyen Philippe Julien
Jennifer Bordeaux Adrian Pasculescu Alexander Roth
Thomas Sicheritz-Pontén Jin Gyoon Park Milan Simonovic
Marina Olhovsky Leona D. Samson Jan Korbel
Adrian Pasculescu Rob Russell Berend Snel
Jes Alexander Peer Bork Martijn Huynen
Stefan Knapp Michael Yaffe Peer Bork
Nikolaj Blom Tony Pawson
Peer Bork
Shawn Li
Gianni Cesareni
Tony Pawson
Benjamin E. Turk
Michael B. Yaffe
Søren Brunak

This document discusses advanced bioinformatics methods for proteomics, specifically focusing on three parts: signaling networks, association networks, and text mining. It describes using phosphoproteomics to map signaling networks and identify unknown kinases and binding proteins. It also discusses using databases like STRING to map association networks through computational predictions, experimental data, and curated knowledge from pathways. Finally, it covers using natural language processing and text mining to extract information from the large amounts of unstructured text data in order to recognize entities and extract relations to help understand proteomics.

Advanced bioinformatics methods for proteomics

Lars Juhl Jensen

This document discusses advanced bioinformatics methods for proteomics, specifically focusing on three parts: signaling networks, association networks, and text mining. It describes using phosphoproteomics to map signaling networks and identify unknown kinases and binding proteins. It also discusses using databases like STRING to map association networks through computational predictions, experimental data, and curated knowledge from pathways. Finally, it covers using natural language processing and text mining to extract information from the large amounts of biomedical literature in order to recognize entities and extract relations to help understand proteomics data.

Mining text and data on chemicals

Lars Juhl Jensen

This document summarizes Lars Juhl Jensen's work on text mining of chemicals and medical data. It discusses three parts: (1) using text mining to identify relevant papers on specific topics from the exponentially growing literature, (2) integrating data from multiple sources on chemicals, proteins, genomes and their interactions, and (3) extracting information from unstructured patient records written by doctors. The goal is to develop natural language processing methods to formalize extracted facts and relationships to aid in tasks like pharmacovigilance and disease comorbidity analysis.

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Protein-protein interaction

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The document discusses various strategies for targeting protein-protein interactions (PPIs) for drug development, including small molecule inhibitors, peptides, and allosteric modulation. PPIs are important for many cellular pathways but have been challenging to target with drugs. Examples are given of peptides being developed into peptidomimetics to inhibit PPIs like integrins. Fragment-based approaches and structure-based design have also produced PPI inhibitors for targets like interleukin-2 receptor. Covalent modifiers and stabilizing allosteric sites are additional strategies discussed.

STRING - Protein networks from data and text mining

Lars Juhl Jensen

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.

Protein–protein interaction networks

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Protein protein interactions

Prasanthperceptron

S.Prasanth Kumar provides information on analyzing protein-protein interactions, specifically focusing on homodimers and heterodimers. Key analysis methods discussed include calculating the change in solvent accessible surface area between monomeric and multimeric states, assessing planarity and complementarity of interacting surfaces, and determining residue propensities at interfaces. The summary emphasizes that most homodimers only exist in multimeric states and hydrophobic and polar interactions between subunits are important for protein association.

This document discusses advanced bioinformatics methods for proteomics, specifically focusing on three parts: signaling networks, association networks, and text mining. It describes using phosphoproteomics to map signaling networks and identify unknown kinases and binding proteins. It also discusses using databases like STRING to map association networks through computational predictions, experimental data, and curated knowledge from pathways. Finally, it covers using natural language processing and text mining to extract information from the large amounts of biomedical literature in order to recognize entities and extract relations to help understand proteomics data.

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Lars Juhl Jensen

Ppi

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Protein-protein interaction

sigma-tau

STRING - Protein networks from data and text mining

Lars Juhl Jensen

Protein–protein interaction networks

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Protein protein interactions

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Protein-Protein Interactions (PPIs)

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The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.

Protein protein interaction basic

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Proteins often interact with other proteins to carry out their functions. There are two types of protein interactions: stable and transient. Protein interactions can alter enzyme kinetics, allow substrate channeling, create new binding sites, inactivate proteins, or change protein specificity. Studying protein interactions experimentally is difficult, time-consuming, inaccurate, costly and lacks necessary equipment. Databases like STRING and PINA integrate known and predicted protein interaction data from multiple sources to better understand protein interaction networks.

Protein protein interactions

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Protein-protein interactions occur when two or more proteins bind together to carry out biological functions. Researchers map these interactions to build interactome networks that provide insight into cellular processes. Two main methods to detect interacting proteins are yeast two-hybrid systems and co-immunoprecipitation. Interactome networks along with other data are stored in databases to analyze how genes and proteins work together in pathways and disease states. Mapping the full interactome is challenging but will further biological understanding beyond what is known from genomic data alone.

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Proteomics ppt

Shashikala Chinnaswamy

Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.

Proteomics

Dimitri Raptis

Proteomics is the large-scale study of proteins, including their structures, functions, and interactions. It has become an important technology for understanding biological systems on a global scale. Mass spectrometry plays a key role in proteomic analysis by allowing researchers to identify and characterize proteins and their post-translational modifications like phosphorylation. There are challenges in analyzing post-translational modifications since proteins exist in multiple modified forms, but methods like affinity enrichment and tandem mass spectrometry are used to map modifications and locate them on protein sequences.

Network biology - Large-scale data integration and text mining

Lars Juhl Jensen

This document discusses network biology and large-scale data integration and text mining. It discusses three parts: signaling networks, association networks, and text mining. Signaling networks involve proteomics and PTMs, while association networks include STRING and experimental data. Text mining involves named entity recognition, information extraction, and co-mentioning to integrate data from the over 10,000 biomedical articles published each month that is too much for humans to read alone. It acknowledges contributions from researchers involved in databases and tools like NetPhorest, NetworKIN, and STRING.

Network biology: Large-scale data integration and text mining

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Advanced bioinformatics methods for proteomics

Lars Juhl Jensen

The document discusses several advanced bioinformatics methods and computational tools for proteomics research, including NetPhorest, STRING, Reflect, and NetworKIN. It describes how these tools use machine learning techniques and large protein-protein interaction datasets to build models for predicting kinase-substrate relationships and mapping sequence motifs, helping to organize big proteomics data and generate testable hypotheses to guide experimental validation work.

Networks of proteins and diseases

Lars Juhl Jensen

The document discusses networks of proteins and diseases. It is divided into three parts that discuss protein networks, localization of proteins and their relationship to diseases, and disease networks. It describes several databases of protein interactions and issues with comparing them. It then discusses how text mining can extract protein localization and disease information to build networks linking proteins to compartments, tissues, and diseases. It also describes how mining electronic health records can build networks of disease comorbidities and trajectories. In the end it thanks collaborators on several relevant projects.

Large-scale data and text mining

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This document discusses Lars Juhl Jensen's work in large-scale data and text mining, including developing tools for predicting protein function, analyzing cell cycle regulation and phosphorylation signaling networks, compiling datasets on protein interactions and functional associations, and integrating data across multiple species for genomic and proteomic analysis. It acknowledges contributions from colleagues on projects including NetPhorest, STRING, STITCH, NetworKIN, and Reflect.

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Unraveling signaling networks by large-scale data integration

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Protein networks: A basis for large-scale data mining

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The document discusses protein networks and how they can be used as a basis for large-scale data mining. It describes three phases: 1) constructing association networks using various data types, 2) building signaling networks by identifying signaling events and sequence motifs, and 3) examining dynamic networks to understand when interactions occur, such as during cell cycle regulation. The goal is to integrate different data sources to generate specific predictions and gain insights into systems properties and functions.

Protein networks: A basis for large-scale data mining

Lars Juhl Jensen

Protein networks: A basis for large-scale data mining

Lars Juhl Jensen

This document discusses protein networks and how they can be used for large-scale data mining. It describes three phases: 1) constructing association networks using different data types like genomic context and protein interactions, 2) building signaling networks using phosphoproteomics and sequence motifs, and 3) analyzing dynamic networks on the temporal level using microarray time courses and evolutionary flexibility of individual genes and protein complexes. The overall goal is to integrate different data sources to generate specific predictions and gain systems-level properties and insights.

Network biology: Large-scale biomedical data and text mining

Lars Juhl Jensen

The document discusses network biology and large-scale biomedical data mining. It covers three main topics: protein networks, localization of proteins to compartments and tissues and their association with diseases, and disease networks. It describes several databases and resources that integrate experimental data, computational predictions, and curated knowledge to build networks, but notes they use different formats, identifiers, and quality. Text mining approaches are discussed that extract protein and disease information from literature to project onto networks and study relationships. The document also discusses using structured and unstructured electronic health records to build disease networks and study comorbidities and diagnosis trajectories.

Protein networks: A basis for large-scale data mining

Lars Juhl Jensen

The document discusses protein interaction networks and their use as a basis for large-scale data mining. It describes three phases: 1) building association networks using computational predictions, experimental data and curated knowledge, 2) constructing signaling networks using phosphoproteomics data to map signaling events, and 3) developing dynamic networks to study temporal protein interactions and cell cycle regulation using time course microarray data. The networks integrate different data sources to generate specific predictions and provide insights into systems properties and evolutionary flexibility.

Systems biology: Large-scale biomedical data mining

Lars Juhl Jensen

The document discusses large-scale systems biology and biomedical data mining. It describes three phases of research: 1) constructing association networks from genomic and protein interaction data, 2) building signaling networks by predicting kinase-substrate relationships using phosphoproteomics and machine learning, and 3) analyzing dynamic networks to understand temporal regulation of cellular processes using time-course data. The overall goal is to extract specific predictions and insights into systems properties through data mining approaches.

Large-scale integration of data and text

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This document discusses large-scale integration of data and text in bioinformatics. It describes using text mining on millions of abstracts and articles to extract information on biological entities and their associations in order to build networks of proteins, genes, diseases and small molecules. This information is integrated with experimental data and computational predictions into web-centric databases and resources that can help researchers by saving them time over manually reviewing the literature. Visualization tools are also provided to project network data onto tissue and subcellular localization information extracted from text.

Protein networks: A basis for large-scale data mining

Lars Juhl Jensen

The document discusses protein interaction networks and their use in large-scale data mining. It describes three phases: 1) constructing association networks using genomic context, co-expression, and curated protein interactions. 2) Building signaling networks using phosphoproteomics, sequence motifs, and prediction tools. 3) Analyzing dynamic networks using time course microarray data to study temporal networks and just-in-time assembly during the cell cycle. The goal is to integrate diverse data types to generate networks and make specific predictions about systems properties.

Network biology - Large-scale biomedical data and text mining

Lars Juhl Jensen

This document discusses network biology and large-scale biomedical data and text mining. It is divided into three parts. The first part discusses protein networks, including data from over 1100 genomes and different data types and databases with variable quality. The second part discusses literature mining of over 10km of text to extract concepts like proteins and diseases through named entity recognition and information extraction. It describes a website for abstracts and collaborating with publishers. The third part discusses medical informatics, including analyzing structured and unstructured electronic health records written in Danish by doctors about psychiatric patients to study comorbidities, patient clustering, and temporal medication correlations for pharmacovigilance. It acknowledges contributions from researchers at various universities and institutions.

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Advanced bioinformatics methods for proteomics

1. Advanced bioinformatics methods for proteomics Lars Juhl Jensen

2. three parts

3. signaling networks

4. association networks

5. text mining

6. Part 1 signaling networks

7. phosphoproteomics

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

9. in vivo phosphosites

10. kinases are unknown

11. sequence specificity

12. Miller, Jensen et al., Science Signaling, 2008

13. NetPhorest

14. automated pipeline

15. Miller, Jensen et al., Science Signaling, 2008

16. motif atlas

17. kinases

18. phospho-binding proteins

19. phosphatases

20. protein-specific

21. no context

22. co-activators

23. protein scaffolds

24. localization

25. expression

26. association network

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

28. NetworKIN

29.

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

31. Part 2 association networks

32. guilt by association

33.

34. STRING

35. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

36. >1100 genomes

37. computational predictions

38. genomic context

39. gene fusion

40. Korbel et al., Nature Biotechnology, 2004

41. phylogenetic profiles

42. Korbel et al., Nature Biotechnology, 2004

43. experimental data

44. physical interactions

45. Jensen & Bork, Science, 2008

46. genetic interactions

47. Beyer et al., Nature Reviews Genetics, 2007

48. gene coexpression

49.

50. curated knowledge

51. pathways

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

53. many data types

54. many databases

55. different formats

56. different identifiers

57. variable quality

58. not comparable

59. spread over 1100+ genomes

60. quality scores

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

62. calibrate vs. gold standard

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

64. orthology transfer

65. missing most of the data

66. Part 3 text mining

67. >10 km

68. too much to read

69. computer

70. as smart as a dog

71. teach it specific tricks

72.

73.

74. named entity recognition

75. identify the concepts

76. proteins

77. small molecules

78. comprehensive lexicon

79. orthographic variation

80. “black list”

81. Reflect

82. augmented browsing

83. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010

84. information extraction

85. formalize the facts

86. co-mentioning

87.

88. global statistical analysis

89. NLP Natural Language Processing

90. parsing individual sentences

91. summary

92. computational biology

93. data mining

94. text mining

95. save you much time

96. Acknowledgments NetPhorest NetworKIN STRING Reflect Rune Linding Rune Linding Christian von Mering Sune Frankild Martin Lee Miller Heiko Horn Damian Szklarczyk Heiko Horn Erwin Schoof Gerard Ostheimer Michael Kuhn Evangelos Pafilis Francesca Diella Martin Lee Miller Manuel Stark Michael Kuhn Claus Jørgensen Francesca Diella Samuel Chaffron Nigel Brown Michele Tinti Karen Colwill Chris Creevey Reinhardt Lei Li Jing Jin Jean Muller Schneider Marilyn Hsiung Pavel Metalnikov Tobias Doerks Sean O’Donoghue Sirlester A. Parker Vivian Nguyen Philippe Julien Jennifer Bordeaux Adrian Pasculescu Alexander Roth Thomas Sicheritz-Pontén Jin Gyoon Park Milan Simonovic Marina Olhovsky Leona D. Samson Jan Korbel Adrian Pasculescu Rob Russell Berend Snel Jes Alexander Peer Bork Martijn Huynen Stefan Knapp Michael Yaffe Peer Bork Nikolaj Blom Tony Pawson Peer Bork Shawn Li Gianni Cesareni Tony Pawson Benjamin E. Turk Michael B. Yaffe Søren Brunak

97. larsjuhljensen

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