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341: Introduction to Bioinformatics Dr. Nataša Pržulj Department of Computing Imperial College London Winter 2011 1.

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Presentation on theme: "341: Introduction to Bioinformatics Dr. Nataša Pržulj Department of Computing Imperial College London Winter 2011 1."— Presentation transcript:

1 341: Introduction to Bioinformatics Dr. Nataša Pržulj Department of Computing Imperial College London Winter

2 2 2 Topics Introduction to biology (cell, DNA, RNA, genes, proteins) Sequencing and genomics (sequencing technology, sequence alignment algorithms) Functional genomics and microarray analysis (array technology, statistics, clustering and classification) Introduction to biological networks Introduction to graph theory Network properties  Network/node centralities  Network motifs Network models Network/node clustering Network comparison/alignment Software tools for network analysis Interplay between topology and biology 2

3 Network Comparisons: Properties of Large Networks Large network comparison is computationally hard due to NP- completeness of the underlying subgraph isomorphism problem: Given 2 graphs G and H as input, determine whether G contains a subgraph that is isomorphic to H.subgraphisomorphic Thus, network comparisons rely on easily computable heuristics (approximate solutions), called “network properties” Network properties can roughly & historically be divided in two categories: 1.Global network properties: give an overall view of the network, but might not be detailed enough to capture complex topological characteristics of large networks. 2.Local network properties: more detailed network descriptors which usually encompass larger number of constraints, thus reducing degrees of freedom in which the networks being compared can vary. 3

4 1. Global Network Properties Readings: Chapter 3 of “Analysis of biological networks” by Junker and Björn Global Network Properties: 1)Degree distribution 2)Average clustering coefficient 3)Clustering spectrum 4)Average Diameter 5)Spectrum of shortest path lengths 6)Centralities 4

5 1)Degree Distribution Definitions: degree of a node is the number of edges incident to the node. Average degree of a network: average of the degrees over all nodes in the network. However, avg. deg might not be representative, since the distribution of degrees might be skewed Global Network Properties x deg(x)=5

6 Degree distribution:  Let P(k) be the percentage of nodes of degree k in the network. The degree distribution is the distribution of P(k) over all k.  P(k) can be understood as the probability that a node has degree k Global Network Properties 1) Degree Distribution

7 Example: (log-log plot)  Here P(k) ~ k -γ, where often 2 ≤ γ < 3. This is a power-law, heavy-tailed distribution.  Networks with power-law degree distributions are called scale-free networks. In them, most of the nodes are of low degree, but there is a small number of highly-linked nodes (nodes of high degree) called “hubs.” 7 1. Global Network Properties 1) Degree Distribution

8 Another Example: average degree is meaningful Here P(k) is a Poisson distribution Global Network Properties 1) Degree Distribution

9 However: degree distribution (and global properties in general) are weak predictors of network structure. Illustration: G and H are of the same size (i.e.,|G|=|H| -- they have the same number of nodes and edges) and they have same degree distribution, but G and H have very different topologies (i.e., graph stucture) Global Network Properties 1) Degree Distribution

10 G Examples:

11 11 Research debates… Assortative vs. disassortative mixing of degrees: – Do high-degree nodes interact with high-degree nodes? – Done by: – Pearson corr. coefficient between degrees of adjacent vertices – Average neighbor degree; then average over all nodes of degree k Structural robustness and attack tolerance: – “Robust, yet fragile” Scale-free degree distribution: – “Party” vs. “date” hubs J.D. Han et al., Nature, 430:88-93, 2004 – Bias in the data collection – sampling? M. Stumpf et al., PNAS, 102: , 2005 J. Han et al., Nature Biotechnology, 23: , 2005 High degree nodes: – Essential genes H. Jeong at al., Nature 411, – Disease/cancer genes Jonsson and Bates, Bioinformatics, 22(18), 2006 Goh et al., PNAS, 104(21), 2007

12 Definition: clustering coefficient C v of a node v: C v = |E(N(v))|/(max possible number of edges in N(v)) Where N(v) the neighborhood of v, i.e., all nodes adjacent to v C v can be viewed as the probability that two neighbors of v are connected. Thus 0 ≤ C v ≤ 1. By definition: For vertex v of degree 0 or 1, by definition C v = Global Network Properties 2) Average Clustering Coefficient

13 Example:  |N(v)|= 4, since there are 4 nodes in N(v), i.e., N(v)= {1, 2, 3, 4}  |E(N(v))|= 3, since there are 3 edges between nodes in N(v)  Max possible number of edges between nodes in N(v) is: choose(4,2) = 6.  Therefore C v = 3/6 = 1/ Global Network Properties 2) Average Clustering Coefficient

14 Definition: average clustering coefficient, C, of a network is the average C v over all the nodes v ∈ V Global Network Properties 2) Average Clustering Coefficient

15 Definition: clustering spectrum, C(k), is the distribution of the average clustering coefficients of all nodes of degree k in the network, over all k. Example: Global Network Properties 3) Clustering Spectrum

16 C v – Clustering coefficient of node v C A = 1/1 = 1 C B = 1/3 = 0.33 C C = 0 C D = 2/10 = 0.2 … C = Avg. clust. coefficient of the whole network = avg {C v over all nodes v of G} C(k) – Avg. clust. coefficient of all nodes of degree k E.g.: C(2) = (C A + C C )/2 = (1+0)/2 = 0.5 => Clustering spectrum E.g. (not for G ) 2) And 3) Clustering Coefficient and Spectrum G Need to evaluate whether the value of C (or any other property) is statistically significant.

17 17 Definition: the distance between two nodes is the smallest number of links that have to be traversed to get from one node to the other. Definition: the shortest path is the path that achieves that distance. Definition: the average network diameter is the average of shortest path lengths over all pairs of nodes in a network. 1. Global Network Properties 4) Average Diameter

18 Definition: Let S(d) be the percentage of node pairs that are at distance d. The spectrum of shortest path lengths is the distribution of S(d) over d. Example: Global Network Properties 5) Spectrum of shortest path lengths

19 4) and 5) Average Diameter and Spectrum of Shortest Path Lengths G u v E.g. (not for G) Distance between a pair of nodes u and v: D u, v = min {length of all paths between u and v} = min {3,4,3,2} = 2 = dist(u,v) Average diameter of the whole network: D = avg {D u,v for all pairs of nodes {u,v} in G} Spectrum of the shortest path lengths

20 ( Readings: Chapter 3 of “Analysis of biological networks”-Junker,Björn) Rank nodes according to their “topological importance” Definition: – Centrality quantifies the topological importance of a node (edge) in a network. There are many different types of centralities. There are many different types of centralities: – Degree centrality – Closeness centrality – Eccentricity centrality – Betweenness centrality – Subgraph centrality – Eigenvector centrality Software tools: Visone (social nets) and CentiBiN (biological nets) Global Network Properties 6) Node Centralities

21 Definitions : 1.Degree centrality, C d (v): nodes with a large number of neighbors (i.e., edges) have high centrality. Therefore, we have C d (v)=deg(v). Example of a use of degree centrality: In PPI networks, nodes with high degree centrality are considered to be “biologically important.” We will learn later in the course what this means. 2.Closeness centrality, C c (v): nodes with short paths to all other nodes in the network have high closeness centrality C c (v)= Global Network Properties 6) Node Centralities

22 22 Definitions: 3. Betweenness centrality, C b (v): Nodes (or edges) which occur in many of the shortest paths have high betweeness centrality. C b (v)= Above: The above summation means that there is a sum on the top and on the bottom of the fraction. σ st (v) = the number of shortest paths from s to t that pass through v σ st = the number of all shortest paths from s to t (they may or not pass through node v) Global Network Properties 6) Node Centralities

23 23 Definitions: 4. Eccentricity centrality, C e (v): nodes with short paths to any other node have high eccentricity centrality Eccentricity of a node v is defined as ecc(v) = So it is the maximum shortest path length from node u to all other nodes v in V. Eccentricity centrality of a node v: Thus, central nodes have higher C e since they have lower ecc. There exist many other definitions of node centralities Global Network Properties 6) Node Centralities

24 Example: 24 DegreeClosenessBetweeness From highestDF, GH D, HF, G toA, B I C, E, HC, ED lowestIIA, B JJC, D, J 1. Global Network Properties 6) Node Centralities

25 You need to know how to compute these centralities (and all other network properties) by hand on small networks. For large real-world networks, you could use software, e.g., CentiBiN. – Global Network Properties 6) Node Centralities

26 26 Network Properties 2. Local Network Properties ( Chapter 5 of the course textbook “Analysis of Biological Networks” by Junker and Schreiber) They encompass a larger number of constraints, thus reducing degrees of freedom in which networks being compared can vary How do we show that two networks are different? How do we show that they are the same? How do we quantify the level of similarity?

27 27 Network Properties 2. Local Network Properties ( Chapter 5 of the course textbook “Analysis of Biological Networks” by Junker and Schreiber) 1)Network motifs 2)Graphlets Two network comparison measures based on graphlets: 2.1) Relative Graphlet Frequence Distance between two networks 2.2) Graphlet Degree Distribution Agreement between two networks

28 (Uri Alon’s group, ) Definition: A network motif is a small over- represented partial subgraph of real network. Here, over-represented means that it is over- represented when compared to networks coming from a random graph model. Problem: What is expected at random, i.e., which network “null model” to use to identify motifs? Local Network Properties 1) Network Motifs

29 29 2. Local Network Properties 1) Network Motifs Example of a random graph model: Erdos-Renyi (ER) random graphs – Definition: – A graph on n nodes (for some positive integer n) – Edges are added between pairs of nodes uniformly at random with same probability p ER graphs usually have a small number of dense (in term of number of edges) subgraphs  There will be no regions in the network that have large density of edges. Why?

30 Example: If motifs are identified when comparing the data with ER model networks, every dense subgraph would come up as a motif because they do not exist in our ER model networks Local Network Properties 1) Network Motifs

31 31 Small subgraphs that are overrepresented in a network when compared to randomized networks Network motifs: – Reflect the underlying evolutionary processes that generated the network – Carry functional information – Define superfamilies of networks  - Z i is statistical significance of subgraph i, SP i is a vector of numbers in 0-1 But: – Functionally important but not statistically significant patterns could be missed – The choice of the appropriate null model is crucial, especially across “families” 1) Network motifs (Uri Alon’s group, ’02-’04) Feed-forward loop

32 32 Small subgraphs that are overrepresented in a network when compared to randomized networks Network motifs: – Reflect the underlying evolutionary processes that generated the network – Carry functional information – Define superfamilies of networks  - Z i is statistical significance of subgraph i, SP i is a vector of numbers in 0-1 But: – Functionally important but not statistically significant patterns could be missed – The choice of the appropriate null model is crucial, especially across “families” –Random graphs with the same in- and out- degree distribution as data might not be the best network null model –Motifs are partial subgraphs, while we use induced ones to understand network structure 1) Network motifs (Uri Alon’s group, ’02-’04)

33 33 2. Local Network Properties 1) Network Motifs Example: Feed-forward loop Shen-Orr, Milo, Mangan, and Alon, “Network motifs in the transcriptional regulation network of Escherichia coli,” Nature Genetics, 2002

34 34 1) Network motifs (Uri Alon’s group, ’02-’04) Also, see Pajek, MAVisto, and FANMOD

35 N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg , _____ Different from network motifs:  Induced subgraphs  Of any frequency (don’t need to be over-represented) 2) Graphlets (Przulj group, ’04-’10)

36 N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg , 2004.

37 N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg , 2004.

38 N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale Free or Geometric?,” Bioinformatics, vol. 20, num. 18, pg , ) Relative Graphlet Frequency (RGF) distance between networks G and H:

39 Generalize node degree 2.2) Graphlet Degree Distributions

40 N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” ECCB, Bioinformatics, vol. 23, pg. e177-e183, 2007.

41

42 T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures”, Cancer Informatics, vol. 4, pg , Network structure vs. biological function & disease Graphlet Degree (GD) vectors, or “node signatures”

43 Similarity measure between “node signature” vectors T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures”, Cancer Informatics, vol. 4, pg , 2008.

44 Signature Similarity Measure between nodes u and v

45 T. Milenković and N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008: , 2008 (Highly Visible).

46 40% SMD1 PMA1 YBR095C T. Milenković and N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008: , 2008 (Highly Visible).

47

48 90%* SMD1 SMB1 RPO26 T. Milenković and N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008: , 2008 (Highly Visible). *Statistically significant threshold at ~85%

49 Later we will see how to use this and other techniques to link network structure with biological function

50 N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” Bioinformatics, vol. 23, pg. e177-e183, Generalize Degree Distribution of a network The degree distribution measures: the number of nodes “touching” k edges for each value of k

51 N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” Bioinformatics, vol. 23, pg. e177-e183, 2007.

52

53 / sqrt(2) (  to make it between 0 and 1) This is called Graphlet Degree Distribution (GDD) Agreement between networks G and H.

54 Software that implements many of these network properties and compares networks with respect to them: GraphCrunch

55 Software that implements many of these network properties and compares networks with respect to them: GraphCrunch

56 56 Topics Introduction to biology (cell, DNA, RNA, genes, proteins) Sequencing and genomics (sequencing technology, sequence alignment algorithms) Functional genomics and microarray analysis (array technology, statistics, clustering and classification) Introduction to biological networks Introduction to graph theory Network properties  Network/node centralities  Network motifs Network models Network/node clustering Network comparison/alignment Software tools for network analysis Interplay between topology and biology 56


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