Tools for Network Biology 2

2. 관심 있는 Gene Set에서 시작하는 일반적인 네트워크 분석(MONGKIE)

3. 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 relation
Network Visualization
MONGKIE
Network Clustering
clique percolation method (소개만)
MCODE
MCL
Sub-network annotation
Sub-network를 이루는 유전자들의 개별조사(NCBI EntrezGene)
생물학자의 조언
관련 Canonical pathway 조사 (Enrichment Analysis)
text mining을 이용한 문헌조사,,,,,,

4. 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

5. Sample data
Gene Set
Table S3.
Promoter Regions bound
by Sox2 in ES cells

6. hESC :1750
ChIP-on-chip Data generation
- Used to investigate interactions
between proteins and DNA in vivo hESC : Young Lab(MIT)
SOX2 Target Prediction
http://en.wikipedia.org/wiki/ChIP-on-chip

7. SOX2 Cofactor Analysis
- TRANSFAC Annotation Data
- TRANSFAC
Match(Prediction)
OR
TSS
SOX2
Gene a mR A
i N
Protein A
ESC Regulation

8. Expression Data
GSE1535
5
platform - Illumina HumanRef-8 v3.0 expression beadchip

9. ESC - Significant Cofactor

10. • Data Import
• Data-to-Visual Mapping and Editors
• Network Layout
• Network Clustering and Grouping
• GO Enrichment Analysis
• Expression Overlay
• Data Export

11. • 실행파일
– DesktopMongkie_20120228mongkiebinmo
ngkie.exe
• 다운로드
– http://wiki.kobic.re.kr/display/openspace/Mong
kie

12. Data Import

13. Import CSV: Node table

14. Import CSV: Edge table

15. Import Report

16. Imported Network

17. Data-to-Visual Mapping
• Map data attributes of nodes and edges to
the visual properties
– Continuous map
– Discrete map

18. Continuous Visual Mapping: Import expression data

19. Continuous Visual Mapping: Color

20. Continuous Visual Mapping: Color

21. Continuous Visual Mapping: Size

22. Continuous Visual Mapping: Size

23. Continuous Visual Mapping: Spline Editor

24. Discrete Visual Mapping

25. Discrete Visual Mapping

26. Visual Editors for Nodes and Edges

27. Global Display Options

28. Node, Edge and Group Display Options

29. Network Search and Exploring
• Instant Search
• Network 탐색
Dragging, Panning, Zoom in/out/fit, Neighbor Highlighting, Single/Multiple
selection and Overview display

30. Data Table

31. Network Layout
• Grid
• Circle
• Force Directed
• Radial Tree
• Random

32. Force Directed Layout

33. Force Directed Layout

34. Radial Tree Layout

35. Radial Tree Layout

36. Network Clustering
• MCODE
– Molecular COmplex Detection algorithm -
Bader and Hogue(2003)
• MCL
– Markov CLustering Algorithm

37. 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 graph
Step 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)

38. MCODE

39. MCODE

40. MCL

41. MCL

42. Group Editor

43. GO Enrichment Analysis

44. GO Enrichment Analysis

45. GO Enrichment Analysis

46. GO Enrichment Analysis

47. Expression Overlay

48. Expression Overlay

49. Export: XML, CSV and Images

50. http://mongkie.org