Parallel Detection of Regulatory Elements with gMP

1. Parallel Detection of Regulatory Elements with gMP Bertil Schmidt, Lin Feng, Amey Laud, Yusdi Santoso Damayanti Gupta CMSC 838 Presentation

2. Motivation • Fundamental question • How are expression levels of thousands of genes regulated ? • Very important • Understanding of gene function • Response to environment • Understand genetic causes of diseases • Evaluate effects of drus • Detect mutations • Remember • Sets of genes -> Pathways -> Genetic Networks • Gene regulation • Control decisions turn genes on/off • Gene Regulation Network CMSC 838T – Presentation

3. Talk Overview • Overview of talk • Motivation • Technique • Experiment • Related work • Conclusions CMSC 838T – Presentation

4. Technique • Motifs upstream of genes regulate gene expression • Motifs are sites of regulatory activity • Identify regulatory motifs by combining • Gene expression data • Detect common motifs occuring upstream of genes • Huge datasets • Utilise parallel computing CMSC 838T – Presentation

5. Technique • gRNA • Java development framework • gMP • Java communication library • REDUCE • Algorithm to identify regulatory motifs • REDUCE parallelised with gMP • Increase computing power • Get motifs ranked in statistical significance CMSC 838T – Presentation

6. gRNA framework • Consists of APIs CMSC 838T – Presentation

7. gRNA - APIs • Interact with data sources • Provide functionality from biology • Pipelines tasks into unified process • Repository of resources • Distributed programming CMSC 838T – Presentation

8. gRNA environment • gRNA Grid • Clustered computing environment • Application written for gRNA • Multiple-tier application • Applications operate from client computer • Communicates with cluster through single computer • Hosts EJB server • Server identifies processing nodes • each of these perform tasks CMSC 838T – Presentation

9. gRNA Grid CMSC 838T – Presentation

10. gMP • Java based message passing tool • Built on top of sockets • Manages virtual processors to run on available machines • Scalable • Machines added/removed easily CMSC 838T – Presentation

11. gMP • Processes are grouped • Communication primitives provided for sending and receiving data • Collective communication to several nodes enabled modularly and efficiently • Enables functions to be implemented on data CMSC 838T – Presentation

12. REDUCE algorithm • Based on model • Upstream motifs contribute additively to expression level of each gene • Quantify the extent to which these motifs contribute to expression data • Fit log of expression ratio to sum of activating and inhibitory terms • Find stastically most significant motifs • Plots of fitting parameters suggest biological function CMSC 838T – Presentation

13. REDUCE algorithm • Terms • Occurence vector • Measure of how often a motif is found • Expression vector • Measure of gene expression CMSC 838T – Presentation

14. REDUCE method Consists of 1) Motif frequency counter • counts occurrences of DNA motifs upstream of each ORF • motifs are about 7~11 nucleotides in length • get occurence vectors CMSC 838T – Presentation

15. REDUCE algorithm 2) Significant motif finder • Use i) Normalised occurrence vector made for each motif nμ ii) Normalised vector of logs of gene expression ratio vectors- a • Take dot product of these (a . nμ) ,and square. • Can be considered as frequency of occurence X expressive power of regulatory motif • It is squared to get rid of negatives • Correlate gene expression with occurence of motif • Largest dot product is most significant motif CMSC 838T – Presentation

16. .... • a is modified to remove effect of this motif • residual gene expression vector • Process repeated until motifs are ranked CMSC 838T – Presentation

17. Table: Finding significant motifs • Uses a - (.5816,.2522,.2886,-.5947, -.1595, -.3683) CMSC 838T – Presentation

18. REDUCE parallelised with gMP... • Parallel motif frequency counter • Split set of ORFs equally • Distribute across available nodes • Each node calculates in parallel to get occurence vectors • Matrix transposition • Occurence vectors scattered across nodes • Advantageous to store each vector in single node • Transpose motif frequency matrix • For each ORF can only calculate fraction of occurence frequencies for all motifs • But the entire occurence frequency is needed CMSC 838T – Presentation

19. ... • Parallel significant motif finder • Normalises occurence vector within each node • At each node, most significant motif calculated • Global most significant motif calculated • Process iterated to rank occurence vectors • Interface in gRNA allows ease of implementation CMSC 838T – Presentation

20. Experiment • Use Compaq Alpha system • Consists of cluster of 8 AlphaServer SC/ES45 • Connected by high-speed Alpha SC 16-Port switch and ELAN PCI adapter cards. • Each server contains 4 Alpha EV68 processors CMSC 838T – Presentation

21. Results • Use 7090 gene expressions of yeast • ORFs of length 600 • Motifs upto length 7 • Throughput (in MBytes/s) also shown • 20 most significant motifs computed. CMSC 838T – Presentation

22. Analysis • Runtime scales well with number of processing nodes • Frequency counter scales perfectly • Motif finder also scales • Cannot achieve perfect scaling because of communication overhead. CMSC 838T – Presentation

23. Related work • DiscoveryLink • Provides configurable wrappers as interfaces to multiple data sources • Kleisli system • Systematically manages and integrates external databases • Uses functional query language to perform correlation across databases • Toolkits designed with functionality for specialised areas • BioJava, BioPerl, PAL • Sequence Analysis • Ensembl initiative, DAS • provide extensible approach to issue of annotating genomic data CMSC 838T – Presentation

24. Related work Previous approaches using Java for high performance computing • Bindings into native message-passing APIs(e.g.MPI) • Does not allow easy integration into larger Java applications • Pure Java message passing interfaces • JMPI, CCJ • Both implemented on top of Java RMI • Slower than using raw sockets • CCJ tries to overcome • optimised RMI implementation • not portable • Both cannot handle integration CMSC 838T – Presentation

25. Comparison According to authors ... • gRNA distinguishes itself • Uses whole range of requirements for applications in computational biology • Provides decoupled, yet inter-related subsystems • Ease of 3rd party implementation CMSC 838T – Presentation

26. Observations • REDUCE surpasses traditional clustering approach • REDUCE algorithm has high runtime • Complexity depends on product of number possible motifs and that of genes. • Grows exponentially with length of sequences • So length of motif is restricted • REDUCE algorithm is greedy • suboptimal • REDUCE is simplistic • lacks parameters for interactions between motifs • does not consider impact of other biological knowledge CMSC 838T – Presentation

27. ... • Not clear that results of REDUCE are biologically significant • Experiment does not effectively show how higher computation power helps results • Only analysis from 9 to 16 processors, is this sufficient to determine ‘good scaling’? CMSC 838T – Presentation

28. Conclusions Finally... • gRNA demonstrates efficient mechanism for development of genome-centric applications Further... • Extensions to REDUCE have been proposed • require higher computing power • more specialised programming interfaces required • Identifying communication patterns • Use of data structures e.g. sequences, trees, matrices CMSC 838T – Presentation