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Interpretation of the biological knowledge using networks approach

Interpretation of the biological knowledge using networks approach

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Interpretation of the biological knowledge using networks approach

  1. 1. Interpretation of the biological knowledge using networks approach Elena Sügis elena.sugis@.ut.ee Bioinformatics for bioengineers LTTI.00.016, Spring 2018
  2. 2. lots of experiments v analysis Science knowledge hypothesis v v lots of experiments v analysis Science knowledge hypothesis v v Networks - the language of complex systems Image 2 is adapted from http://www.jillkgregory.com/new-gallery-17/Image 1 is adapted from https://en.wikipedia.org/wiki/Complex_network
  3. 3. Image 2 is adapted from http://www.jillkgregory.com/new-gallery-17/ lots of experiments v analysis Science knowledge hypothesis v v lots of experiments v analysis Science knowledge hypothesis v v Networks-the language of complex systems Image 1 is adapted from https://en.wikipedia.org/wiki/Complex_network
  4. 4. Networks are powerful tools Analysis • Topological properties • Hubs and subnetworks • Classify, cluster and diffuse • Data integration Visualization • Data overlays • Layouts and animation • Exploratory analysis • Context and interpretation Image is adapted from Cassar, EMBO Reports 2015, Fig.8
  5. 5. • Reduce complexity
 • More efficient than tables
 • Great for data integration
 • Intuitive visualization Benefits of using networks
  6. 6. 6 3 4 5 2 1 • NODES • EDGES Graphs are mathematical structure composed of set of objects where pairs of the objects are connected by links Networks can be built for any functional system Networks - are graphs
  7. 7. • Genes • Proteins • Metabolites • Enzymes • Organisms 6 3 4 5 2 1 Nodes The nodes in the networks represent related objects
  8. 8. Biological relationships: • Interactions • Regulations • Reactions • Transformations • Activations • Inhibitions etc. Edges The edges in the network represent the type of relationship between two entities A B A B A B A B activates binds to has similar sequence co-cited
  9. 9. Edges A B A B A B directed undirected weighted 0,8 The architecture (or topology) of a network can be represented as graph with links between the parts.
  10. 10. Image is adapted from https://www.systemsbiology.org/about/what-is-systems-biology/ Interactome With networks, we can organize and integrate information at different levels
  11. 11. Networks in research
  12. 12. Pathways NETWORKS PATHWAYS Collection of binary interactions Human-curated, detailed Large scale Small scale Generated from omics data Constructed from literature/domain expert knowledge A pathway is a series of actions among molecules in a cell that leads to a certain product or a change in a cell.
  13. 13. You want to know: - Type of relationships between genes - Strength of relationship - Functions of the related genes - Pathways - etc. Gene list from experiment APP PSEN1 FYN MAPT BIN1 EPHA1 EPHA2 PSEN What network can tell you
  14. 14. What network can tell you You can: • Visually identify relationships among the group of biological entities • Find drag targets • Identify overrepresented gene/protein functions • Discover biological pathways Alzheimer’s disease
  15. 15. • Series of molecular cancer profiles • Clinical, genomic, methylation, RNA and proteomic signatures. • Multiple data types integrated into signalling network • Includes patient sample-level data Image is adapted from TCGA (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499, Fig. 4 Networks application in research
  16. 16. Sources for network data
  17. 17. Data comes in different forms Computational data -
 results of the analysis Raw data - results of the experiments
 Sequencing technologies Mass spectrometry healthy cell cancer cell DNA RNA Protein co-expression differential expression
  18. 18. DATA≠KNOWLEDGE
  19. 19. Big hairball
  20. 20. Big hairball Nice and clear they say Reduce complexity they say
  21. 21. Biological networks rarely tell us anything by themselves Analysis involves: • Understanding the characteristics of the network • Modularity • Comparison with other networks (i.e., random networks) Visualization involves: • Placing nodes in a meaningful way (layouts) • Mapping biologically relevant data to the network • Change node size, colour, edge weights, etc.
 which allows better biological interpretation. Making sense of the biological networks
  22. 22. Analysis tools Awesome resultData Analysis pipeline
  23. 23. Network analysis tools intro medium advanced
  24. 24. Network analysis tools intro medium advanced hands-on session
  25. 25. Cytoscape APPs zoo
  26. 26. Network properties Global Network Properties Local Network Properties • Degree distribu-on • Clustering coefficient • Shortest path • Centrali-es • Network mo-fs Figure is adapted from https://cytoscape.github.io/cytoscape-tutorials/presentations/advanced-automation-2017-mpi.html#/11
  27. 27. Global Network Properties
  28. 28. Degree distribution Degree of a node is the number of edges incident to the node.
  29. 29. Degree distribution Degree of a node is the number of edges incident to the node.
  30. 30. Degree distribution Degree of a node is the number of edges incident to the node.
  31. 31. Degree distribution Degree of a node is the number of edges incident to the node. 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. P(k) ~ e−λ λk k! Image is adapted from E. Ravasz et al., Science, 2002
  32. 32. Degree distribution in scale-free networks • Networks with power-law degree distributions are called scale-free networks • Most nodes are of low degree, but there is a small number of highly-linked nodes (nodes of high degree) called “hubs.” P(k) ~ k−γ Image is adapted from E. Ravasz et al., Science, 2002
  33. 33. Clustering coefficient Clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Ci=2Ei/ki(ki-1) ith node has ki neighbours linking with it Ei is the actual number of links between ki neighbours ki(ki-1)/2 maximal number of links between ki neighbours Clustering coefficient of a vertex in a graph quantifies how close its neighbours are to be a clique (complete graph)
  34. 34. Clustering coefficient Clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Ci=2Ei/ki(ki-1) ith node has ki neighbours linking with it Ei is the actual number of links between ki neighbours ki(ki-1)/2 maximal number of links between ki neighbours Clustering coefficient of a vertex in a graph quantifies how close its neighbours are to be a clique (complete graph)
  35. 35. Clustering coefficient Clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Ci=2Ei/ki(ki-1) ith node has ki neighbours linking with it Ei is the actual number of links between ki neighbours ki(ki-1)/2 maximal number of links between ki neighbours Clustering coefficient of a vertex in a graph quantifies how close its neighbours are to be a clique (complete graph)
  36. 36. Clustering coefficient Clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Ci=2Ei/ki(ki-1) ith node has ki neighbours linking with it Ei is the actual number of links between ki neighbours ki(ki-1)/2 maximal number of links between ki neighbours Clustering coefficient of a vertex in a graph quantifies how close its neighbours are to be a clique (complete graph)
  37. 37. Clustering coefficient Clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Ci=2Ei/ki(ki-1) ith node has ki neighbours linking with it Ei is the actual number of links between ki neighbours ki(ki-1)/2 maximal number of links between ki neighbours Clustering coefficient of a vertex in a graph quantifies how close its neighbours are to be a clique (complete graph)
  38. 38. Hierarchical modularity Many highly connected small clusters combine into few larger but less connected clusters combine into even larger and even less connected clusters Clustering coefficient follows power-law distributionC(k) ~ k−β
  39. 39. Comparison of the network properties Image is adapted from E. Ravasz et al., Science, 2002 C(k) ~ k−β P(k) ~ k−γ P(k) ~ e−λ λk k!
  40. 40. Shortest path • Distance between two nodes is the smallest number of links that have to be traversed to get from one node to the other.
 Shortest path is the path that achieves that distance.
 • Small world network is characterised by small average path length l = 2 N(N −1) lij i<j ∑ lij is the shortest path length between node i and j
  41. 41. Defining important nodes in biological networks How would you define an important node?
  42. 42. Defining important nodes in biological networks the most connected? connects other nodes in the network? the closest to other nodes?
  43. 43. Centrality Centrality quantifies the topological importance of a node (edge) in a network. • Degree centrality defined number of edges incident upon a node (find hubs). C D (node) = Degree of this node
 
 • Betweenness centrality indicates how much load is on a node (bottleneck). C B (node) = The average number of shortest paths that go through this node 
 • Closeness centrality defines how close a node is to all other nodes in the network. C C (node) = Inverse of the average of the shortest paths to all other nodes. https://cytoscape.github.io/cytoscape-tutorials/presentations/modules/network-analysis/index.html#/0/6
  44. 44. Figure is partially adapted with modifications from original https://cytoscape.github.io/cytoscape-tutorials/presentations/modules/network-analysis/index.html#/0/6 How different centralities look HUB node that connect two sub-networks closest node to all other nodes
  45. 45. Biological meaning Degree centrality Closeness centralityBetweenness centrality • Amount of control that this node has over the interactions of other nodes in the network
 • How much information load is on the node
 • Describes connectivity of the network
 • Nodes that connect two sub-networks
 • Can be calculated for edges as well • Nodes with a high degree are also called hub nodes
 • Real networks have many nodes with low degree and few nodes with high degree
 • Nodes with a high degree tend to be essential nodes
 • Regulatory elements like transcription factors often have a high out-degree • Indication for how fast information spreads from a given node to other reachable nodes in the network • The more central a node is, the smaller is the distance to all other nodes, the higher is the closeness Material is adapted from BioSB 2015 Network Analysis Course
  46. 46. Brain connectivity • A few regions that link the left and the right half of our brain • They therefore have a high betweenness AS. Panditet al, Cerebral Cortex (2014) Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth
  47. 47. Biological networks • Free-scale networks (tend to have power-law degree distribution) • “Small world” networks (small average path length)
 • Have hierarchical modularity property (have a high clustering coefficient independent of network size) • Robustness (have strong resistance to failure on random attacks and vulnerable to targeted attacks)
  48. 48. Local Network Properties
  49. 49. Pattern (sub-networks) that occurs more often than in randomised networks Network motifs Different types of network show different motifs. Gene regulatory networks with transcription factors have typical regulation motifs.
  50. 50. Motifs in yeast regulatory network Image is adapted from Lee et al. Transcriptional Regulatory Networks in Saccharomyces cerevisiae, Science 2002
  51. 51. Motifs in yeast regulatory network • consists of a regulator that binds to the promoter region of its own gene
 • reduced response time to environmental stimuli
 • decreased cost of regulation
 • increased stability of gene expression
  52. 52. Motifs in yeast regulatory network • consists of a regulatory circuit whose closure involves two or more factors 
 • provides the capacity for feedback control 
 • offers the potential to produce bistable systems that can switch between two alternative states
  53. 53. Motifs in yeast regulatory network • contains a regulator that controls a second regulator and both regulators bind a common target gene
 • acts as a switch that is designed to be sensitive to sustained inputs 
 • provides control of expression of target gene depending on the accumulation of adequate levels of the master and secondary regulators
  54. 54. Motifs in yeast regulatory network v • contains a single regulator that binds a set of genes under a specific condition • is responsible for some particular biological function v
  55. 55. Motifs in yeast regulatory network v v • set of regulators that bind together to a set of genes • coordinates gene expression across a wide variety of biological conditions
 • two different regulators responding to two different inputs allow coordinate expression of the set of genes under two different conditions
  56. 56. Motifs in yeast regulatory network v • consists of chains of three or more regulators in which one regulator binds the promoter for a second regulator and so on • simplest ordering of transcriptional events
 • regulators functioning at one stage of the cell cycle regulate the expression of factors required for entry into the next stage of the cell cycle
  57. 57. Community detection in biological networks
  58. 58. Community detection Figure is adapted from original https://cytoscape.github.io/cytoscape-tutorials/presentations/advanced-automation-2017-mpi.html#/11 Identifying closely-related groups of nodes (modules/clusters) • Based on topology • Based on a shared function(s)
  59. 59. Hub-based modules Module contains a node with high degree and its first neighbours
  60. 60. Clique modules Module contains nodes that are all connected between each other
  61. 61. MCL-based modules • Flow simulation based method • Consider a graph with many links within a cluster, and fewer links between clusters. • This means if you were to start at a node, and then randomly travel to a connected node, you’re more likely to stay within a cluster than travel between. • By doing random walks in the graph, it may be possible to discover. where the flow tends to gather, and therefore, where clusters are • Random Walks on a graph are calculated using “Markov Chains”. Image is adapted from https://micans.org/mcl/
  62. 62. Betweenness-centrality based modules Algorithm step-wise removes edges (nodes) with the highest betweenness-centrality
  63. 63. Quiz
  64. 64. Quiz
  65. 65. Quiz
  66. 66. Group functional characterisation
  67. 67. Functional enrichment Your gene
 list • Each module contains a list of genes. • You want to know the biological story behind this module.
  68. 68. Functional characterisation Identify biological function of the module Cellular component Molecular function Biological process Gene Ontology KEGG Reactome Pathways Regulation miRBase miRNAs TRANSFAC TF targets Biogrid PPIs CORUM protein complexes Human Phenotype Ontology Extra
  69. 69. Functional enrichment Genes with known
 function x ? Your gene
 list
  70. 70. Functional enrichment Does your gene list includes more genes with function x than expected by random chance? Genes with known
 function x ? Your gene
 list
  71. 71. Tool for functional enrichment http://biit.cs.ut.ee/gprofiler J. Reimand, M. Kull, H. Peterson, J. Hansen, J. Vilo: g:Profiler - a web-based toolset for functional profiling of gene lists from large-scale experiments (2007) NAR 35 W193-W200 
 Jüri Reimand, Tambet Arak, Priit Adler, Liis Kolberg, Sulev Reisberg, Hedi Peterson, Jaak Vilo: g:Profiler -- a web server for functional interpretation of gene lists (2016 update) Nucleic Acids Research 2016; doi: 10.1093/nar/gkw199
  72. 72. 2175 modules found Enrichment results for example module https://biit.cs.ut.ee/graphweb/ Example of module functional characterisation
  73. 73. Clustering based on enriched function http://apps.cytoscape.org/apps/cluego
  74. 74. Questions & Answers https://www.sli.do/ #P783 Ask a question Vote for a question Open browser Go to www.slido.com Enter code #P783 4 5

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