Introduction to Bioinformatics Lecture 19 Intracellular Networks Graph theory C E N T R F O R I N T...
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Transcript of Introduction to Bioinformatics Lecture 19 Intracellular Networks Graph theory C E N T R F O R I N T...
Introduction to Bioinformatics
Lecture 19Lecture 19
Intracellular NetworksIntracellular NetworksGraph theoryGraph theory
CENTR
FORINTEGRATIVE
BIOINFORMATICSVU
E
High-throughput Biological Data
Enormous amounts of biological data are being generated by high-throughput capabilities; even more are coming– genomic sequences
– gene expression data
– mass spectrometry data
– protein-protein interaction data
– protein structures
– ......
Hidden in these data is information that reflects – existence, organization, activity, functionality …… of biological
machineries at different levels in living organisms
Bio-Data Analysis andData Mining
Existing/emerging bio-data analysis and mining tools for– DNA sequence assembly
– Genetic map construction
– Sequence comparison and database search
– Gene finding
– ….
– Gene expression data analysis
– Phylogenetic tree analysis to infer horizontally-transferred genes
– Mass spec. data analysis for protein complex characterization
– …… Current prevailing mode of work
Developing ad hoc tools for each individual application
Bio-Data Analysis and Data Mining
As the amount and types of data and the needs to establish connections across multi-data sources increase rapidly, the number of analysis tools needed will go up “exponentially”
– blast, blastp, blastx, blastn, … from BLAST family of tools– gene finding tools for human, mouse, fly, rice, cyanobacteria, …..– tools for finding various signals in genomic sequences, protein-binding sites,
splice junction sites, translation start sites, …..
Many of these data analysis problems are fundamentally the same problem(s) and can be solved using the same set of tools
Developing ad hoc tools for each application problem (by each group of individual researchers) may soon become inadequate
as bio-data production capabilities further ramp up
Data Clustering Many biological data analysis problems can be formulated
as clustering problems– microarray gene expression data analysis– arrayCGH data (chromosomal gains and losses)– identification of regulatory binding sites (similarly, splice junction
sites, translation start sites, ......)– (yeast) two-hybrid data analysis (for inference of protein
complexes)– phylogenetic tree clustering (for inference of horizontally
transferred genes)– protein domain identification– identification of structural motifs– prediction reliability assessment of protein structures– NMR peak assignments – ......
Data Clustering: an example Regulatory binding-sites are short conserved sequence fragments in
promoter regions
Solving binding-site identification as a clustering problem– Project all fragments into Euclidean space so that similar fragments are
projected to nearby positions and dissimilar fragments to far positions– Observation: conserved fragments form “clusters” in a noisy background
........acgtttataatggcg ......
........ggctttatattcgtc ......
........ccgaatataatcta .......
Data Clustering Problems
Clustering: partition a data set into clusters so that data points of the same cluster are “similar” and points of different clusters are “dissimilar”
Cluster identification -- identifying clusters with significantly different features than the background
Multivariate statistics – Cluster analysis
12345
C1 C2 C3 C4 C5 C6 ..
Raw tableAny set of numbers per column
•Multi-dimensional problems
•Objects can be viewed as a cloud of points in a multidimensional space
•Need ways to group the data
Multivariate statistics – Cluster analysis
Dendrogram
Scores
Similaritymatrix
5×5
12345
C1 C2 C3 C4 C5 C6 ..
Raw table
Similarity criterion
Cluster criterion
Any set of numbers per column
Cluster analysis – data normalisation/weighting
12345
C1 C2 C3 C4 C5 C6 ..
Raw table
Normalisation criterion
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C1 C2 C3 C4 C5 C6 ..
Normalised table
Column normalisation x/max
Column range normalisation (x-min)/(max-min)
Cluster analysis – (dis)similarity matrix
Scores
Similaritymatrix
5×5
12345
C1 C2 C3 C4 C5 C6 ..
Raw table
Similarity criterion
Di,j = (k | xik – xjk|r)1/r Minkowski metrics
r = 2 Euclidean distancer = 1 City block distance
Cluster analysis – Clustering criteria
Dendrogram (tree)
Scores
Similaritymatrix
5×5
Cluster criterion
Single linkage - Nearest neighbour
Complete linkage – Furthest neighbour
Group averaging – UPGMA (phylogeny)
Ward
Neighbour joining – global measure (phylogeny)
Cluster analysis – Clustering criteria
1. Start with N clusters of 1 object each
2. Apply clustering distance criterion iteratively until you have 1 cluster of N objects
3. Most interesting clustering somewhere in between
Dendrogram (tree)
distance
N clusters1 cluster
Single linkage clustering (nearest neighbour)
Char 1
Char 2
Distance from point to cluster is defined as the smallest distance between that point and any point in the cluster
Single linkage clustering (nearest neighbour)
Single linkage dendrograms typically show chaining behaviour (i.e., all the time a single object is added to existing cluster)
Let Ci and Cj be two disjoint clusters:
di,j = Min(dp,q), where p Ci and q Cj
Complete linkage clustering (furthest neighbour)
Char 1
Char 2
Distance from point to cluster is defined as the largest distance between that point and any point in the cluster
Complete linkage clustering (furthest neighbour)
More ‘structured’ clusters than with single linkage clustering
Let Ci and Cj be two disjoint clusters:
di,j = Max(dp,q), where p Ci and q Cj
Clustering algorithm
1. Initialise (dis)similarity matrix2. Take two points with smallest distance
as first cluster 3. Merge corresponding rows/columns in
(dis)similarity matrix4. Repeat steps 2. and 3.
using appropriate clustermeasure until last two clusters are merged
Average linkage clustering (Unweighted Pair Group Mean Averaging -UPGMA)
Char 1
Char 2
Distance from cluster to cluster is defined as the average distance over all within-cluster distances
UPGMA
Let Ci and Cj be two disjoint clusters:
1di,j = ———————— pq dp,q, where p Ci and q Cj
|Ci| × |Cj|
In words: calculate the average over all pairwise inter-cluster distances
Ci Cj
Multivariate statistics – Cluster analysis
Phylogenetic tree
Scores
Similaritymatrix
5×5
12345
C1 C2 C3 C4 C5 C6 ..
Data table
Similarity criterion
Cluster criterion
Multivariate statistics – Cluster analysis
Scores
5×5
12345
C1 C2 C3 C4 C5 C6
Similarity criterion
Cluster criterion
Scores
6×6
Cluster criterion
Make two-way ordered
table using dendrograms
Multivariate statistics – Two-way cluster analysis
14253
C4 C3 C6 C1 C2 C5
Make two-way (rows, columns) ordered table using dendrograms; This shows ‘blocks’ of numbers that are similar
Graph theory
The river Pregal in Königsberg – the Königsberg bridge problem and Euler’s graph
Can you start at some land area (S1, S2, I1, I2) and walk each bridge exactly once returning to the starting land area?
Graphs - definition
Digraphs: Directed graphs
Complete graphs: have all possible edges
Planar graphs: can be presented in 2D and have no crossing edges (e.g. chip design)
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1 0 2 1 6.5 6 8 8
1.5 2 0 1 4 4 6 5.5
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Graph Adjacency matrix
Graphs - definition
An undirected graph has a symmetric adjacency matrix
A digraph typically has a non-symmetric adjacency matrix
Example application – OBSTRUCT: creating
non-redundant datasets of protein structures Based on all-against-all global sequence alignment Create all-against-all sequence similarity matrix Filter matrix based on desired similarity range
(convert to ‘0’ and ‘1’ values) Form maximal clique (largest complete subgraph) by
ordering rows and columns This is an NP-complete problem (NP = non-
polynomial) and thus problem scales exponentially with number of vertices (proteins)
Example application 1 – OBSTRUCT: creating non-redundant datasets of protein
structures • Statistical research on protein structures typically
requires a database of a maximum number of non-redundant (i.e. non-homologous) structures
• Often, two structures that have a sequence identity of less than 25% are taken as non-redundant
• Given an initial set of N structures (with corresponding sequences) and all-against-all pair-wise alignments:
• Find the largest possible subset where each sequence has <25% sequence identity with any other sequence
Heringa, J., Sommerfeldt, H., Higgins, D., and Argos, P. (1992). Obstruct: a program to obtain largest cliques from a protein sequence set according to structural resolution and sequence similarity. Comp. Appl. Biosci. (CABIOS) 8, 599-600.
Example application 1 – OBSTRUCT: creating non-redundant datasets of protein
structures (Cnt.)• The problem now can be formalised as follows:
• Make a graph containing all sequences as vertices (nodes)
• Connect two nodes with an edge if their sequence identity < 25%
• Make an adjacency matrix following the above rules
Heringa, J., Sommerfeldt, H., Higgins, D., and Argos, P. (1992). Obstruct: a program to obtain largest cliques from a protein sequence set according to structural resolution and sequence similarity. Comp. Appl. Biosci. (CABIOS) 8, 599-600.
Example application 1 – OBSTRUCT: creating non-redundant datasets of protein
structures (Cnt.)
The algorithm:
• Now try and reorder the rows (and columns in the same way) such that we get a square only consisting of 1’s in the upper left corner
• This corresponds to a complete graph (also called clique) containing a set of non-redundant proteins
Heringa, J., Sommerfeldt, H., Higgins, D., and Argos, P. (1992). Obstruct: a program to obtain largest cliques from a protein sequence set according to structural resolution and sequence similarity. Comp. Appl. Biosci. (CABIOS) 8, 599-600.
Example application 1 – OBSTRUCT: creating non-redundant datasets of protein
structures (Cnt.)
1 0 1 1 1 0 0 0 1
0 1 0 0 1 1 1 0 0
1 0 1 1 1 0 1 1 0
1 0 1 1 0 0 0 0 1
. . . . . . .
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..
Adjacency matrix
1. Order sum array and reorder rows and columns accordingly…
2. Estimate largest possible clique and take subset of adj. matrix containing only rows with enough 1s
3. For a clique of size N, a subset of M rows (and columns), where M N, with at least N 1s is selected.
4. Go to step 1.
Heringa, J., Sommerfeldt, H., Higgins, D., and Argos, P. (1992). Obstruct: a program to obtain largest cliques from a protein sequence set according to structural resolution and sequence similarity. Comp. Appl. Biosci. (CABIOS) 8, 599-600.
Some books call graphs containing multiple edges or loops a multigraph, and those without a graph. Other books allow multiple edges or loops in a graph, but then talk about a graph without multiple edges and loops as a simple graph.
Remarks
A multigraph might have no multiple edges or loops. Every (simple) graph is a multigraph, but not every multigraph is a (simple) graph.
Every graph is finite
Sometimes even “multigraph” folks talk about a “simple graph” to emphasize that there are no multiple edges and loops.
Further definitions
K3,3
bipartite A graph is bipartite if its vertices can be partitioned into two disjoint subsets U and V such that each edge connects a vertex from U to one from V. A bipartite graph is a complete bipartite graph if every vertex in U is connected to every vertex in V. If U has n elements and V has m, then we denote the resulting complete bipartite graph by Kn,m.
The Stable Marriage Algorithm
Given two non-overlapping equally sized graphs of men (A, B, C, ..) and women (a, b, c, …), where each man and woman has a preference list about persons of the opposite sex
A pairing denotes a 1-to-1 correspondence between men and women (each man marries one woman)
A pairing is unstable if there are couples X-x and Y-y such that X prefers y to x and y prefers X to Y– if this happens, pair X-y is called unsatisfied
A pairing in which there are no unsatisfied couples is called a stable pairing or stable marriage
The Stable Marriage Algorithm forms a bipartite graph that is stable
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1 0 2 1 6.5 6 8 8
1.5 2 0 1 4 4 6 5.5
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Graph Adjacency matrix
Graphs - definition
An undirected graph has a symmetric adjacency matrix
A digraph typically has a non-symmetric adjacency matrix
A Theoretical Framework Representation of a set of n-dimensional (n-D) points as a graph
– each data point represented as a node – each pair of points represented as an edge with a weight defined by the
“distance” between the two points
0 1 1.5 2 5 6 7 9
1 0 2 1 6.5 6 8 8
1.5 2 0 1 4 4 6 5.5
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n-D data pointsgraph
representationdistance matrix
A Theoretical Framework
Spanning tree: a sub-graph that has all nodes connected and has no cycles
Minimum spanning tree: a spanning tree with the minimum total distance
(a) (b) (c)
Spanning tree Prim’s algorithm (graph, tree)
– step 1: select an arbitrary node as the current tree – step 2: find an external node that is closest to the tree, and add it with its
corresponding edge into tree– step 3: continue steps 1 and 2 till all nodes are connected in tree.
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35
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(e)
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35
(b)
4 4
(c)
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3
(d)
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(a)
Kruskal’s algorithm– step 1: consider edges in non-decreasing order – step 2: if edge selected does not form cycle, then add it into tree; otherwise
reject– step 3: continue steps 1 and 2 till all nodes are connected in tree.
(f)
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35
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35
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(a) (b)
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(c)
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Spanning tree
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(d)
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(e)
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reject
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(e)
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(e)
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A Theoretical Framework A formal definition of a cluster:
– C forms a cluster in D only if for any partition C = C1 U C2, the closest point, from D-C1, to C1 is from C2.
Key results c1
c2
For any data set D, any of its cluster is represented by a sub-tree of its MST
A Theoretical Framework The selection order of nodes by PRIM’s algorithm defines a linear
representation, L(D), of a data set D
Any contiguous block in L(D) represents a cluster if and only if its elements form a sub-tree of the MST, plus
some minor additional conditions (each cluster forms a valley)
Valleys correspond to clusters (red bars)
Application Examples Regulatory binding site identification: cAMP-receptor protein (CRP)
binding site
Two hybrid data analysis Gene expression data analysis
Are all solvable by the same algorithm
More Application Examples
Phylogenetic tree clustering analysis
Protein sidechain packing prediction
Assessment of prediction reliability of protein structures
Protein secondary structures
NMR peak assignments
……
Example 2: Graph-based clustering: REPRO
Heringa, J., and Argos P. (1993). A method to recognize distant repeats in protein sequences. Proteins Struct. Func. Genet. 17, 391-411.
Non-supervised algorithm for finding repeats in protein sequences, where
Repeats can be evolutionary distant (low sequence similarity)
Multiple sets of repeats can be recognised
Graph-based clustering: Repro
1. Calculate top-scoring non-overlapping local alignments
2. Stacking of local alignments3. Make graph with N-termini of top-
alignments as nodes4. Perform graph-based clustering
Heringa, J., and Argos P. (1993). A method to recognize distant repeats in protein sequences. Proteins Struct. Func. Genet. 17, 391-411.