1. A P ARALLEL A LGORITHM FOR E XTRACTING T RANSCRIPTIONAL R EGULATORY N ETWORK M OTIFS Fu Rong Wu

2. O UTLINE Preliminary Previous Work Method Experimental Result Conclusion

3. B IOLOGICAL 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

4. T RANSCRIPTIONAL R EGULATORY N ETWORK describe the interactions between transcription factor proteins and the genes that they regulate

5. B IOLOGICAL N ETWORK MOTIFS E XAMPLE Autoregulation (AR) Feed Forward Loops (FFL) Regulating and Regulated Feedback Loops (RFL) BiFan Diamond

6. O UTLINE Preliminary Previous Work Method Experimental Result Conclusion

7. P REVIOUS W ORK 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

8. O UTLINE Preliminary Previous Work Method Experimental Result Conclusion

9. METHOD Goal: Find motif from a given graph G(V,E) One Master Processor Sort all nodes by degree Partition nodes to Slave processors Slave Processors Finding Neighborhoods from a Network Finding Subgraphs within Neighborhood Gather subgraph set to Master Processor

10. F INDING N EIGHBORHOODS FROM A N ETWORK

12. R EVIEW OF BFS

14. E XAMPLE OF BFS T REE

15. A LGORITHM 1 NBR(G, V )

17. E XAMPLE OF A LGORITHM 1 (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) )

19. E XAMPLE FOR ALGORITHM 2

21. E XAMPLE FOR ALGORITHM 3 Subgraph from (c)

23. O UTLINE Preliminary Previous Work Method Experimental Result Conclusion

24. E XPERIMENTAL RESULT The cluster has 32 machines with two 2.4GHz processors The programs are written in C and MPI library.

25. E XPERIMENTAL 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.

26. E XPERIMENTAL 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)

27. E XPERIMENTAL RESULT For k=6 Total number 15747 motif number 22532584

28. E XPERIMENTAL RESULT

29. O UTLINE Preliminary Previous Work Method Experimental Result Conclusion

30. C ONCLUSION 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.