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

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.