Advanced bioinformaticsof proteomics datasets

•Download as PPT, PDF•

3 likes•1,063 views

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.

Science

Advanced bioinformatics
of proteomics datasets
Lars Juhl Jensen

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

Miller, Jensen et al., Science Signaling, 2008

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Franceschini et al., Nucleic Acids Research, 2013

what you learned in school
pronoun pronoun verb preposition noun

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]

What's hot

Introduction to STRING

Lars Juhl Jensen

Gene association networks: Large-scale integration of data and text

Lars Juhl Jensen

Gene association networks: Large-scale integration of data and text

Lars Juhl Jensen

STRING: Protein networks from data and text mining

Lars Juhl Jensen

Gene association networks - Large-scale integration of data and text

Lars Juhl Jensen

Gene association networks - Large-scale integration of data and text

Lars Juhl Jensen

In silico and Text-Based Analysis of Cellular Networks

Lars Juhl Jensen

Protein association networks: Large-scale integration of data and text

Lars Juhl Jensen

Network biology: Large-scale data and text mining

Lars Juhl Jensen

Protein association networks with STRING

Lars Juhl Jensen

STRING & related databases: Large-scale integration of heterogeneous data

Lars Juhl Jensen

Gene association networks - Large-scale integration of data and text

Lars Juhl Jensen

The STRING database - Quality scores for heterogeneous interaction data

Lars Juhl Jensen

STRING/STITCH tutorial

biocs

The STRING database and related tools

Lars Juhl Jensen

The STRING database

Lars Juhl Jensen

STRING: Large-scale data and text mining

Lars Juhl Jensen

Network Biology: A crash course on STRING and Cytoscape

Lars Juhl Jensen

Network Biology: Large-scale integration of data and text

Lars Juhl Jensen

Large-scale integration of data and text

Lars Juhl Jensen

What's hot (20)

Introduction to STRING

Gene association networks: Large-scale integration of data and text

STRING: Protein networks from data and text mining

Gene association networks - Large-scale integration of data and text

In silico and Text-Based Analysis of Cellular Networks

Protein association networks: Large-scale integration of data and text

Network biology: Large-scale data and text mining

Protein association networks with STRING

STRING & related databases: Large-scale integration of heterogeneous data

Gene association networks - Large-scale integration of data and text

The STRING database - Quality scores for heterogeneous interaction data

STRING/STITCH tutorial

The STRING database and related tools

The STRING database

STRING: Large-scale data and text mining

Network Biology: A crash course on STRING and Cytoscape

Network Biology: Large-scale integration of data and text

Large-scale integration of data and text

Viewers also liked

Proteomics

Shereen Shehata

Proteomics

yashgin66

Proteomics

ruchibioinfo

Proteomics ppt

Shashikala Chinnaswamy

Proteomics

Dimitri Raptis

Proteomics analysis: Basics and ApplicationsCOST action BM1006

Techniques in proteomics

N Poorin

Dawdy_Ferman_Family_Conference_Room _2016Ambermarie Dawdy

Cancer Proteomics

Mereshega Jayasankar

Estrategias purificacao analises proteínas

kantidio

Protein quantitation techniques

Girish Kumar K

Protein isolation and quantification

Jayantyadav94

Proteomics repositories

Juan Antonio Vizcaino

Clinical proteomics in diseases lecture, 2014

Mohammad Hessam Rafiee

Genomics and Proteomics - Impact on Drug Discovery

Philip Bourne

Genomics and proteomics IINikolay Vyahhi

Data Integration, Mass Spectrometry Proteomics Software DevelopmentNeil Swainston

OpenMS: Quantitative proteomics at large scale

Yasset Perez-Riverol

Techniques used for separation in proteomics

Nilesh Chandra

Proteomics course 1utpaltatu

Viewers also liked (20)

Proteomics

Proteomics ppt

Proteomics

Proteomics analysis: Basics and Applications

Techniques in proteomics

Dawdy_Ferman_Family_Conference_Room _2016

Cancer Proteomics

Estrategias purificacao analises proteínas

Protein quantitation techniques

Protein isolation and quantification

Proteomics repositories

Clinical proteomics in diseases lecture, 2014

Genomics and Proteomics - Impact on Drug Discovery

Genomics and proteomics II

Data Integration, Mass Spectrometry Proteomics Software Development

OpenMS: Quantitative proteomics at large scale

Techniques used for separation in proteomics

Proteomics course 1

Similar to Advanced bioinformaticsof proteomics datasets

Large-scale integration of data and text

Lars Juhl Jensen

Systems biology: Bioinformatics on complete biological systemLars Juhl Jensen

The STRING database

Lars Juhl Jensen

Integration of heterogeneous data

Lars Juhl Jensen

Cross-species data integration

Lars Juhl Jensen

Protein interaction networksLars Juhl Jensen

Networks of proteins and diseasesLars Juhl Jensen

Systems biology: Bioinformatics on complete biological systems

Lars Juhl Jensen

Network biology: Large-scale integration of data and text

Lars Juhl Jensen

STRING: Protein association networks

Lars Juhl Jensen

STRING: protein association networks

Lars Juhl Jensen

Network biology: Large-scale data and text miningLars Juhl Jensen

Gene Association Networks: Large-scale integration of data and text

Lars Juhl Jensen

Cellular network biology: Proteome-wide analysis of heterogeneous data

Lars Juhl Jensen

Unraveling cellular phosphorylation networks using computational biology

Lars Juhl Jensen

Information integrationLars Juhl Jensen

Data Integration and Systems Biology

Lars Juhl Jensen

Unraveling signal transduction networks through data integration

Lars Juhl Jensen

Similar to Advanced bioinformaticsof proteomics datasets (18)

Large-scale integration of data and text

Systems biology: Bioinformatics on complete biological system

The STRING database

Integration of heterogeneous data

Cross-species data integration

Protein interaction networks

Networks of proteins and diseases

Systems biology: Bioinformatics on complete biological systems

Network biology: Large-scale integration of data and text

STRING: Protein association networks

STRING: protein association networks

Network biology: Large-scale data and text mining

Gene Association Networks: Large-scale integration of data and text

Cellular network biology: Proteome-wide analysis of heterogeneous data

Unraveling cellular phosphorylation networks using computational biology

Information integration

Data Integration and Systems Biology

Unraveling signal transduction networks through data integration

Recently uploaded

(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...

Scintica Instrumentation

Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes. In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.

insect taxonomy importance systematics and classification

anitaento25

Citrus Greening Disease and its Management

subedisuryaofficial

Structures and textures of metamorphic rocks

kumarmathi863

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

AADYARAJPANDEY1

Introduction: RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression. It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences. dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants. This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes. What are small ncRNAs? micro RNA (miRNA) short interfering RNA (siRNA) Properties of small non-coding RNA: Involved in silencing mRNA transcripts. Called “small” because they are usually only about 21-24 nucleotides long. Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered). Silence an mRNA by base pairing with some sequence on the mRNA. Discovery of siRNA? The first small RNA: In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the development of the worm C. elegans. Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14. Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions. Types of RNAi ( non coding RNA) MiRNA Length (23-25 nt) Trans acting Binds with target MRNA in mismatch Translation inhibition Si RNA Length 21 nt. Cis acting Bind with target Mrna in perfect complementary sequence Piwi-RNA Length ; 25 to 36 nt. Expressed in Germ Cells Regulates trnasposomes activity MECHANISM OF RNAI: First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces. Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands. The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it. THE RISC COMPLEX: RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA Unwinding of double stranded Si RNA by ATP independent Helicase Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA. DICER: endonuclease (RNase Family III) Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC) One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute ARGONAUTE PROTEIN : 1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA 2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity. MiRNA: The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .

platelets_clotting_biogenesis.clot retractionpptx

muralinath2

GBSN - Biochemistry (Unit 5) Chemistry of Lipids

Areesha Ahmad

NuGOweek 2024 Ghent - programme - final version

pablovgd

Leaf Initiation, Growth and Differentiation.pdf

RenuJangid3

platelets- lifespan -Clot retraction-disorders.pptx

muralinath2

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

muralinath2

PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION

ChetanK57

GBSN- Microbiology (Lab 3) Gram Staining

Areesha Ahmad

Mammalian Pineal Body Structure and Also Functions

YOGESH DOGRA

Seminar of U.V. Spectroscopy by SAMIR PANDA

SAMIR PANDA

Comparative structure of adrenal gland in vertebrates

sachin783648

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Erdal Coalmaker

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

Sérgio Sacani

The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.

EY - Supply Chain Services 2018_template.pptx

AlguinaldoKong

Lab report on liquid viscosity of glycerin

ossaicprecious19

Recently uploaded (20)

(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...

insect taxonomy importance systematics and classification

Citrus Greening Disease and its Management

Structures and textures of metamorphic rocks

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

platelets_clotting_biogenesis.clot retractionpptx

GBSN - Biochemistry (Unit 5) Chemistry of Lipids

NuGOweek 2024 Ghent - programme - final version

Leaf Initiation, Growth and Differentiation.pdf

platelets- lifespan -Clot retraction-disorders.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION

GBSN- Microbiology (Lab 3) Gram Staining

Mammalian Pineal Body Structure and Also Functions

Seminar of U.V. Spectroscopy by SAMIR PANDA

Comparative structure of adrenal gland in vertebrates

Unveiling the Energy Potential of Marshmallow Deposits.pdf

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

EY - Supply Chain Services 2018_template.pptx

Lab report on liquid viscosity of glycerin

Advanced bioinformaticsof proteomics datasets

1. Advanced bioinformatics of proteomics datasets Lars Juhl Jensen

2. three parts

3. signaling networks

4. association networks

5. text mining

6. signaling networks

7. MS-based proteomics

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

9. in vivo PTM sites

10. actors are unknown

11. sequence specificity

12. logo plots

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

14. motif collections

15. Eukaryotic Linear Motifs

16. regular expressions

17. [VILMAFP](K).E

18. NetPhorest

19. machine learning

20. kinases

21. phospho-binding proteins

22. phosphatases

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

24. possible actors

25. no cellular context

26. co-activators

27. protein scaffolds

28. localization

29. expression

30. association network

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

32. NetworKIN

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

34. web interface

35.

36. association networks

37. guilt by association

38.

39. STRING

40. 2000+ genomes

41. computational predictions

42. gene fusion

43. Korbel et al., Nature Biotechnology, 2004

44. gene neighborhood

45. Korbel et al., Nature Biotechnology, 2004

46. phylogenetic profiles

47. Korbel et al., Nature Biotechnology, 2004

48. experimental data

49. gene coexpression

50.

51. protein interactions

52. Jensen & Bork, Science, 2008

53. curated knowledge

54. pathways

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

56. many databases

57. different formats

58. different identifiers

59. variable quality

60. not comparable

61. not same species

62. hard work

63. (Ph.D. students)

64. parsers

65. common identifiers

66. clever ideas

67. quality assessment

68. scoring schemes

69. affinity purification

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

71. score calibration

72. gold standard

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

74. implicit weighting by quality

75. common scale

76. interologs

77. homology-based transfer

78. orthologous groups

79. Franceschini et al., Nucleic Acids Research, 2013

80. missing most of the data

81. text mining

82. >10 km

83. too much to read

84. exponential growth

85. ~40 seconds per paper

86. computer

87. as smart as a dog

88. teach it specific tricks

89.

90.

91. named entity recognition

92. comprehensive lexicon

93. CDC2

94. cyclin dependent kinase 1

95. orthographic variation

96. expansion rules

97. prefixes and suffixes

98. CDC2

99. hCdc2

100. flexible matching

101. spaces and hyphens

102. cyclin dependent kinase 1

103. cyclin-dependent kinase 1

104. “black list”

105. SDS

106. information extraction

107. co-mentioning

108. counting

109. within documents

110. within paragraphs

111. within sentences

112. scoring scheme

113.

114.

115. score calibration

116. natural language processing

117. grammatical analysis

118. what you learned in school pronoun pronoun verb preposition noun

119. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]

120. complex sentences

121. summary

122. computational approaches

123. data integration

124. text mining

125. scoring schemes

126. general approach

127. protein networks

128. string-db.org

129. chemical networks

130. stitch-db.org

131. subcellular localization