Toolsfornetworkbiology 2

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Tools for Network Biology 2

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Toolsfornetworkbiology 2

  1. 1. 관심 있는 Gene Set에서 시작하는 일반적인 네트워크 분석(MONGKIE)
  2. 2. 관심있는 Gene Set에서 시작하는 일반적인 네트워크 분석(sample gene set)Example set을 만들어 분석한 내용을 토대로 자료를 만든다.Seeding : Gene set(DEGs, Mutation Gene list, Fusion Gene list)Expanding : 어떤 관계 데이터를 이용하여 네트워크를 그릴 것인가? PPI TF(transcription factor) : TG(target gene) miRNA : TG canonical pathway를 이루는 relationNetwork Visualization MONGKIENetwork Clustering clique percolation method (소개만) MCODE MCLSub-network annotation Sub-network를 이루는 유전자들의 개별조사(NCBI EntrezGene) 생물학자의 조언 관련 Canonical pathway 조사 (Enrichment Analysis) text mining을 이용한 문헌조사,,,,,,
  3. 3. Sample Network NFKB1 RELA Dimer ESC NFKB1 NFKB1 Dimer ESC SP1 PDPN TFregulation ESC SP1 SLC39A1 TFregulation ESC hsa-miR-34a E2F3 miRregulation ESC hsa-miR-34a NOTCH1 miRregulation ESC Node Property SP1 Gene TF ESC SMAD3 Gene TF ESC RELA Gene TF ESC hsa-miR-34a miR miR ESC hsa-miR-34a miR miR ESC hsa-miR-34a miR miR ESC DLX1 soxTarget ESC SSR4 soxTarget ESC
  4. 4. Sample dataGene SetTable S3.Promoter Regions boundby Sox2 in ES cells
  5. 5. hESC :1750 ChIP-on-chip Data generation - Used to investigate interactions between proteins and DNA in vivo hESC : Young Lab(MIT) SOX2 Target Predictionhttp://en.wikipedia.org/wiki/ChIP-on-chip
  6. 6. SOX2 Cofactor Analysis - TRANSFAC Annotation Data - TRANSFAC Match(Prediction) OR TSS SOX2 Gene a mR A i N Protein A ESC Regulation
  7. 7. Expression DataGSE15355 platform - Illumina HumanRef-8 v3.0 expression beadchip
  8. 8. ESC - Significant Cofactor
  9. 9. • Data Import• Data-to-Visual Mapping and Editors• Network Layout• Network Clustering and Grouping• GO Enrichment Analysis• Expression Overlay• Data Export
  10. 10. • 실행파일 – DesktopMongkie_20120228mongkiebinmo ngkie.exe• 다운로드 – http://wiki.kobic.re.kr/display/openspace/Mong kie
  11. 11. Data Import
  12. 12. Import CSV: Node table
  13. 13. Import CSV: Edge table
  14. 14. Import Report
  15. 15. Imported Network
  16. 16. Data-to-Visual Mapping • Map data attributes of nodes and edges to the visual properties – Continuous map – Discrete map
  17. 17. Continuous Visual Mapping: Import expression data
  18. 18. Continuous Visual Mapping: Color
  19. 19. Continuous Visual Mapping: Color
  20. 20. Continuous Visual Mapping: Size
  21. 21. Continuous Visual Mapping: Size
  22. 22. Continuous Visual Mapping: Spline Editor
  23. 23. Discrete Visual Mapping
  24. 24. Discrete Visual Mapping
  25. 25. Visual Editors for Nodes and Edges
  26. 26. Global Display Options
  27. 27. Node, Edge and Group Display Options
  28. 28. Network Search and Exploring • Instant Search • Network 탐색 Dragging, Panning, Zoom in/out/fit, Neighbor Highlighting, Single/Multiple selection and Overview display
  29. 29. Data Table
  30. 30. Network Layout • Grid • Circle • Force Directed • Radial Tree • Random
  31. 31. Force Directed Layout
  32. 32. Force Directed Layout
  33. 33. Radial Tree Layout
  34. 34. Radial Tree Layout
  35. 35. Network Clustering • MCODE – Molecular COmplex Detection algorithm - Bader and Hogue(2003) • MCL – Markov CLustering Algorithm
  36. 36. MCODE Step 1. Vertex Weighting 1-1. Finding neighber 1-2. Get highest k-core graph 1-3. Calculate density of 1-4. Calculate vertex weight k-core graphStep 2. Complex Prediction Step 3. Post-Processing 2-1. Seed complex by nodes 3-1. Complex must contain at with highest weight least a 2-core graph 2.2. Include neighbors if the 3-2. Include neighbors if the vertex weight is above vertex weight is above the threshold(VWP) : vertex fluff parameter(Optional) weight percentage 3-3. Haircut : Remove nodes 2-3. Repeat step 2 until no with a degree less than more nodes can be included two(Optional)
  37. 37. MCODE
  38. 38. MCODE
  39. 39. MCL
  40. 40. MCL
  41. 41. Group Editor
  42. 42. GO Enrichment Analysis
  43. 43. GO Enrichment Analysis
  44. 44. GO Enrichment Analysis
  45. 45. GO Enrichment Analysis
  46. 46. Expression Overlay
  47. 47. Expression Overlay
  48. 48. Export: XML, CSV and Images
  49. 49. http://mongkie.org

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