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A PARALLEL ALGORITHM FOR EXTRACTING TRANSCRIPTIONAL REGULATORY
NETWORK MOTIFS
Fu Rong Wu
OUTLINE
PreliminaryPrevious WorkMethodExperimental ResultConclusion
BIOLOGICAL MOTIFS Sequence motif
a sequence pattern of nucleotides in a DNA sequence or amino acids in a protein
Structural motif a pattern in a protein structure formed
by the spatial arrangement of amino acids Network motif
patterns (sub-graphs) that recur within a network much more often than expected at random
TRANSCRIPTIONAL REGULATORY NETWORK describe the interactions between
transcription factor proteins and the genes that they regulate
BIOLOGICAL NETWORK MOTIFS EXAMPLE
Autoregulation (AR)
Feed Forward Loops (FFL)
Regulating and Regulated Feedback Loops (RFL)
BiFan
Diamond
OUTLINE
PreliminaryPrevious WorkMethodExperimental ResultConclusion
PREVIOUS WORK exhaustive search algorithm
runtime increase dramatically for subgraphs with size ≥ 4.
Impractical to find high-order motifs because of its time complexity.
random sampling algorithm method improves the running time only estimate the frequency of subgraphs cannot
provide an exact solution
OUTLINE
PreliminaryPrevious WorkMethodExperimental ResultConclusion
METHODGoal: Find motif from a given graph
G(V,E) One Master Processor
Sort all nodes by degreePartition nodes to Slave processors
Slave ProcessorsFinding Neighborhoods from a NetworkFinding Subgraphs within NeighborhoodGather subgraph set to Master Processor
FINDING NEIGHBORHOODS FROM A NETWORK
FINDING NEIGHBORHOODS FROM A NETWORK
REVIEW OF BFS
REVIEW OF BFS
EXAMPLE OF BFS TREE
ALGORITHM 1 NBR(G,V)
ALGORITHM 1 NBR(G,V)
EXAMPLE OF ALGORITHM1 (a) A graph G with 8 nodes that are labeled from 1 to
8 (b) The neighborhood of node 1 in G with motif
size k = 4.(Nbr(1) )
EXAMPLE FOR ALGORITHM2
EXAMPLE FOR ALGORITHM3
Subgraph from (c)
OUTLINE
PreliminaryPrevious WorkMethodExperimental ResultConclusion
EXPERIMENTAL RESULT
The cluster has 32 machines with two 2.4GHz processorsThe programs are written in C and MPI library.
EXPERIMENTAL RESULT
Real data set of interactions between transcription factors and operons in an E. coli network from the RegulonDB database
Each protein complex of a transcription factor or a gene is represented by a node.
EXPERIMENTAL RESULT
Precision / Recall Given Truth Positive value(TP), False Positive
value(FP) and False Negative value(FN), Recall = TP/(FN + TP) and Precision = TP/(TP + FP)
EXPERIMENTAL RESULT
For k=6Total number 15747motif number 22532584
EXPERIMENTAL RESULT
OUTLINE
PreliminaryPrevious WorkMethodExperimental ResultConclusion
CONCLUSION This parallel algorithm can accurately
find all high-order network motifs in a fast running time.
High-order motifs provide important information on biological system design.
