Cellular Network Biology: Large-scale integration of data and text

•Download as PPT, PDF•

2 likes•101 views

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.

Science

Lars Juhl Jensen
Cellular Network Biology
Large-scale integration of data and
text

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Junge, Refsgaard & Garde et al., Database, 2017

Kuhn et al., Nucleic Acids Research, 2016stitch-db.org

Binder et al., Database, 2014compartments.jensenlab.org

tissues.jensenlab.org
Santos et al., PeerJ, 2015
Palasca & Santos et al., submitted, 2017

Palasca & Santos et al., submitted, 2017
Human HPA RNA−seq
Human RNA−seq atlas
Human GNF
Human Exon Array
Pig RNA−seq WUR
Pig RNA−seq Aarhus
Pig Array
Rat RNA−seq BodyMap
Rat RNA−seq MIT
Rat Array
Mouse RNA−seq MIT
Mouse RNA−seq Encode
Mouse GNF V3
Mouse GNF
Heart
Intestine
Kidney
Liver
Nervoussystem
Muscle
Lung
Spleen
Lymphnode
Pancreas
Skin
Stomach
Thyroidgland
Bonemarrow
Saliva
Adrenalgland
Urine
Blood
Eye
Gallbladder
Bone
MouseRatPigHuman

diseases.jensenlab.org Frankild et al., Methods, 2015

Thank you
STRING & STITCH
Damian Szklarczyk
Nadezhda Donche
va
John
"Scooter" Morris
Helen Cook
Michael Kuhn
Stefan Wyder
Milan Simonovic
Alexander Roth
Peer Bork
Christian von Merin
g
COMPARTMENTS,
TISSUES &
DISEASES
Alberto Santos
Oana Palasca
Sune Pletscher-
Frankild
Christian Stolte
Janos Binder
Kalliopi Tsafou
Jan Gorodkin
Sean I. O’Donoghue
RAIN
Alexander Junge
Jan C. Refsgaard
Christian Garde
Xiaoyong Pan
Alberto Santos
Ferhat Alkan
Christian Anthon
Christian von Meri
ng
Christopher
Workman
Jan Gorodkin

GWA studies are perhaps most often used for studying the genetic basis of human diseases, but this technology also has great utility for studying the natural variation of other organisms. In this webcast, Ashley Hintz, Field Application Scientist, will discuss the utility of SVS for analyzing plant GWA data, using publicly available SNP data for Arabidopsis thaliana as a case study. Along the way, Ashley will demonstrate how SVS can be used to manage data, analyze population structure, perform genotype QA and ultimately replicate a published genetic association in A. thaliana using EMMAX regression. She will also address the flexibility of SVS for analyzing the genomes of other plant and animal species.

A Distributed Annotation Pipeline for MSSNG

Simon Twigger

Presented at the Analyze Boulder meet up in August, 2015, this presentation introduces the genome annotation pipeline built by BioTeam (https://bioteam.net) in support of Autism Speaks' MSSNG project. Built entirely on Google infrastructure it allows the MSSNG researchers to go from 10 Billion genome variants down to a more manageable 38 Million variants. These are then annotated with known biological information and integrated into the MSSNG Portal (https://research.mss.ng) for subsequent exploration and analysis.

5th RNA-Seq San Francisco Agenda

Diane McKenna

What's hot

Creating a High Performance Cyberinfrastructure to Support Analysis of Illumi...

Larry Smarr

Determining the Human Gut Microbiome Using Genome Sequencing and Dell's Cloud...

Larry Smarr

Cassava genome hub

CIAT

2014 ASPB Presentation- Berardini

Phoenix Bioinformatics

Targeting the $100 genome | Insight, issue 5Team Consulting Ltd

Data sharing - Data management - The SysMO-SEEK Story

Research Information Network

From Expression to Pathways Using Online Tools

Ali Kishk

What's hot (7)

Creating a High Performance Cyberinfrastructure to Support Analysis of Illumi...

Determining the Human Gut Microbiome Using Genome Sequencing and Dell's Cloud...

Cassava genome hub

2014 ASPB Presentation- Berardini

Targeting the $100 genome | Insight, issue 5

Data sharing - Data management - The SysMO-SEEK Story

From Expression to Pathways Using Online Tools

Similar to Cellular Network Biology: Large-scale integration of data and text

Integration of heterogeneous data

Lars Juhl Jensen

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

Lars Juhl Jensen

Large-scale integration of data and text

Lars Juhl Jensen

Informal presentation on bioinformaticsAtai Rabby

Network Biology: Large-scale integration of data and text

Lars Juhl Jensen

The STRING database and related tools

Lars Juhl Jensen

Large-scale integration of data and textLars Juhl Jensen

Building bioinformatics resources for the global community

ExternalEvents

LarsJuhlJensen2020

CphBioscienceCluster

WikiPathways: how open source and open data can make omics technology more us...

Chris Evelo

Text and data integration

Lars Juhl Jensen

Networks of proteins and diseasesLars Juhl Jensen

Network biology: A basis for large-scale biomedical data miningLars Juhl Jensen

The STITCH and Reflect web resources

Lars Juhl Jensen

Data integration and functional association networks

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

Large-scale integration of data and text

Lars Juhl Jensen

Similar to Cellular Network Biology: Large-scale integration of data and text (20)

Integration of heterogeneous data

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

Large-scale integration of data and text

Informal presentation on bioinformatics

Network Biology: Large-scale integration of data and text

The STRING database and related tools

Large-scale integration of data and text

Building bioinformatics resources for the global community

LarsJuhlJensen2020

WikiPathways: how open source and open data can make omics technology more us...

Text and data integration

Networks of proteins and diseases

Network biology: A basis for large-scale biomedical data mining

The STITCH and Reflect web resources

Data integration and functional association networks

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

Large-scale integration of data and text

Recently uploaded

Anemia_ different types_causes_ conditions

muralinath2

Viksit bharat till 2047 India@2047.pptx

rakeshsharma20142015

Citrus Greening Disease and its Management

subedisuryaofficial

Cancer cell metabolism: special Reference to Lactate Pathway

AADYARAJPANDEY1

Normal Cell Metabolism: Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function. Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released. Cell utilize energy in the form of ATP. The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process. Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP. The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos). It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation. If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use. IN CANCER CELL: Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. introduction to WARBERG PHENOMENA: WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside. Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme. WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.

Seminar of U.V. Spectroscopy by SAMIR PANDA

SAMIR PANDA

EY - Supply Chain Services 2018_template.pptx

AlguinaldoKong

platelets_clotting_biogenesis.clot retractionpptx

muralinath2

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Sérgio Sacani

We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and 30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1 . Our search finds no candidates at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to infer the properties of the evolving luminosity function without binning in redshift or luminosity that marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results, and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5 from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical models for evolution of the dark matter halo mass function.

Lab report on liquid viscosity of glycerin

ossaicprecious19

Nutraceutical market, scope and growth: Herbal drug technology

Lokesh Patil

As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.

Lateral Ventricles.pdf very easy good diagrams comprehensive

silvermistyshot

Mammalian Pineal Body Structure and Also Functions

YOGESH DOGRA

GBSN- Microbiology (Lab 3) Gram Staining

Areesha Ahmad

insect taxonomy importance systematics and classification

anitaento25

Structures and textures of metamorphic rocks

kumarmathi863

Large scale production of streptomycin.pptx

Cherry

SCHIZOPHRENIA Disorder/ Brain Disorder.pdf

SELF-EXPLANATORY

Hemoglobin metabolism_pathophysiology.pptx

muralinath2

In silico drugs analogue design: novobiocin analogues.pptx

AlaminAfendy1

erythropoiesis-I_mechanism& clinical significance.pptx

muralinath2

Recently uploaded (20)

Anemia_ different types_causes_ conditions

Viksit bharat till 2047 India@2047.pptx

Citrus Greening Disease and its Management

Cancer cell metabolism: special Reference to Lactate Pathway

Seminar of U.V. Spectroscopy by SAMIR PANDA

EY - Supply Chain Services 2018_template.pptx

platelets_clotting_biogenesis.clot retractionpptx

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Lab report on liquid viscosity of glycerin

Nutraceutical market, scope and growth: Herbal drug technology

Lateral Ventricles.pdf very easy good diagrams comprehensive

Mammalian Pineal Body Structure and Also Functions

GBSN- Microbiology (Lab 3) Gram Staining

insect taxonomy importance systematics and classification

Structures and textures of metamorphic rocks

Large scale production of streptomycin.pptx

SCHIZOPHRENIA Disorder/ Brain Disorder.pdf

Hemoglobin metabolism_pathophysiology.pptx

In silico drugs analogue design: novobiocin analogues.pptx

erythropoiesis-I_mechanism& clinical significance.pptx

Cellular Network Biology: Large-scale integration of data and text

1. Lars Juhl Jensen Cellular Network Biology Large-scale integration of data and text

2. community resources

3. text mining

4. Cytoscape App

5. software demo

6. community resources

7. STRING

8. protein networks

9. string-db.org

10. curated knowledge

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

12. genomic context

13.

14. experimental data

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

16. calibrated quality scores

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

18. RAIN

19. ncRNA networks

20. Junge, Refsgaard & Garde et al., Database, 2017

21. STITCH

22. chemical networks

23. Kuhn et al., Nucleic Acids Research, 2016stitch-db.org

24. drug databases

25. high-throughput screens

26. COMPARTMENTS

27. subcellular localization

28. Binder et al., Database, 2014compartments.jensenlab.org

29. GO annotations

30. sequence-based prediction

31. TISSUES

32. tissue expression

33. tissues.jensenlab.org Santos et al., PeerJ, 2015 Palasca & Santos et al., submitted, 2017

34. expression atlases

35. Palasca & Santos et al., submitted, 2017 Human HPA RNA−seq Human RNA−seq atlas Human GNF Human Exon Array Pig RNA−seq WUR Pig RNA−seq Aarhus Pig Array Rat RNA−seq BodyMap Rat RNA−seq MIT Rat Array Mouse RNA−seq MIT Mouse RNA−seq Encode Mouse GNF V3 Mouse GNF Heart Intestine Kidney Liver Nervoussystem Muscle Lung Spleen Lymphnode Pancreas Skin Stomach Thyroidgland Bonemarrow Saliva Adrenalgland Urine Blood Eye Gallbladder Bone MouseRatPigHuman

36. DISEASES

37. disease associations

38. diseases.jensenlab.org Frankild et al., Methods, 2015

39. curated knowledge

40. missing most of the data

41. text mining

42. >10 km

43. too much to read

44. computer

45. as smart as a dog

46. teach it specific tricks

47.

48.

49. named entity recognition

50. dictionary

51. genes/proteins

52. chemicals

53. cellular compartments

54. tissues

55. diseases

56. synonyms

57. cyclin dependent kinase 1

58. CDC2

59. orthographic variation

60. cyclin dependent kinase 1

61. cyclin-dependent kinase 1

62. CDC2

63. hCdc2

65. SDS

68. open source

70. broadly applicable

71. Cytoscape

72. networks

73. tables

74. visual styles

75.

76. stringApp

77. import from STRING

78. protein query

79. PubMed query

80. disease query

81.

82. subcellular localization

83. tissue expression

84. enrichment analysis

85. Thank you STRING & STITCH Damian Szklarczyk Nadezhda Donche va John "Scooter" Morris Helen Cook Michael Kuhn Stefan Wyder Milan Simonovic Alexander Roth Peer Bork Christian von Merin g COMPARTMENTS, TISSUES & DISEASES Alberto Santos Oana Palasca Sune Pletscher- Frankild Christian Stolte Janos Binder Kalliopi Tsafou Jan Gorodkin Sean I. O’Donoghue RAIN Alexander Junge Jan C. Refsgaard Christian Garde Xiaoyong Pan Alberto Santos Ferhat Alkan Christian Anthon Christian von Meri ng Christopher Workman Jan Gorodkin

Cellular Network Biology: Large-scale integration of data and text

Recommended

Recommended

More Related Content

What's hot

What's hot (7)

Similar to Cellular Network Biology: Large-scale integration of data and text

Similar to Cellular Network Biology: Large-scale integration of data and text (20)

More from Lars Juhl Jensen

More from Lars Juhl Jensen (20)

Recently uploaded

Recently uploaded (20)

Cellular Network Biology: Large-scale integration of data and text