Parallelized Multiple Sequence Alignment on the Public Cloud

1. Parallelized Multiple Sequence Alignment on the Public Cloud Presented by: Dr. G.Sudha Sadasivam Professor, Dept of CSE, PSG College of Technology, Coimbatore Co-authors Mr B. Vijayan, Mr S. Arul Prakash, Mr K.V. Hari Babu Students, BE(CSE), Dept of CSE, PSG College of Technology, Coimbatore

2. Agenda • Sequence alignment • Introduction to Clouds • Approaches for MSA • Problem statement • System Architecture • Illustration of working of the system • Analysis • Experimental results • Conclusion

3. What is Sequence Alignment? • The procedure of comparing two or more sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences. • Uses • For sequence similarity • Phylogenetic tree analysis • Factors – accuracy and speed

4. Cloud computing Provides scalable, on-demand, RT computing services Suitability of cloud for Sequence Alignment • On-demand scalability of cloud makes it suitable for dynamic nature of MSA • Low cost in maintenance of infrastructure for applications • Data and compute parallelism in clouds through map-reduce paradigm facilitates energy efficient and fast MSA.

5. Types of Sequence Alignment • Pair-wise Alignment • Alignment of two sequences • Global –using Needleman Wunsch algorithm. • L G P S S K Q T G K G S _ S R A W D N • | | | | | | | • L N _ A T K S A G K G A I M R L G D A • Local – using Smith Waterman algorithm. • _ _ _ _ _ _ _ _ _ T G K G _ _ _ _ _ _ _ _ _ _ • | | | • _ _ _ _ _ _ _ _ _ A G K G _ _ _ _ _ _ _ _ _ _ • Multiple Sequence Alignment • Alignment of more than two sequences

6. MSA methods N- sequence length; n- number of sequences

7. MSA in cloud • CloudBurst – RMAP • Does not split sequences to load in cloud environment • Not for MSA • No automatic scale up/down of clusters • CLUE- proposal from Maryland University • VM cloning – Snowflock with MPIs

8. Problem statement Time efficient approach to sequence alignment with quality (accuracy) in Cloud • Using hadoop framework • Dynamic approach  accuracy • Data and compute parallelism in hadoop  speed • Blocking and scalability of hadoop • Parallel transfer of sequence splits over the network to remote clusters • Automated scale up/down of clusters based on computational needs of th environment.

9. Needleman Wunsch Algorithm • Initialization F(0, 0) = 0 F(0, i) = −i * d F(j, 0) = −j* d • Main Iteration For each i=1…M and j=1….N • F(i-1,j-1)+s(xi,yj), case 1 • F(i,j) = max F(i-1,j)-d, case 2 • F(i,j-1)-d, case 3 • DIAG, if case 1 • Ptr(i,j) = UP, if case 2 • LEFT, if case 3 Case 1: xi aligns to yi Case 2: xi aligns to gap Case 3: yi aligns to gap s(xi,yj ) = +1 , match -1 , mismatch

10. Needleman Wunsch Algorithm Optimal Alignment A_TA AGTA f(0,0)+s(1,1) =1 F(1,1)=max f(0,1)-1 = -2 f(1,0)-1 = -2‏ = 1(case 1) f(0,1)+s(1,2) =-2 f(0,2)-1 = -3 f(1,1)-1 = 0 Max = 0 (case 3) i=0 1 2 3 4 F(i,j)‏ j=0 1 2 3 A G T A F(i-1,j-1)+s(xi,yj) F(i-1,j)-d F(i,j-1)-d -1 -2 -3 -4 0 A -1 1 0 -1 -2 • F(0, 0) = 0 • F(0, i) = −i * d • F(j, 0) = −j* d T -2 0 0 1 0 A -3 -1 -1 0 2 s(xi,yj ) = +1, match -1, mismatch d=1 • PTR = • DIAG, if case 1 • UP, if case 2 • LEFT, if case 3 Case 1: xi aligns to yi Case 2: xi aligns to gap Case 3: yi aligns to gap

11. Multiple Sequence Alignment • A multiple sequence alignmentis a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. • The input is a set of query sequences that are assumed to have an evolutionary relationship by which they share a lineage and are descended from a common ancestor. • From the resulting multiple sequence alignment , phylogenetic analysis can be conducted to assess the sequences shared evolutionary origins.

12. MSA Approaches • Dynamic programming • Progressive alignment • Iterative approach

13. Dynamic Programming • Direct method for MSA to identify the globally optimal alignment solution . • Computational complexity • n-dimensional equivalent of the pairwise alignment matrix is formed. • The search space increases exponentially with increasing n and is strongly dependent on sequence length(N). • O(Nn)

14. Progressive Alignment • According to guide tree, • Align seq 1 and 2, • Align seq 3 wrt seq 1 and 2, • Align seq 4 to that of seq 1, 2, and 3. seq 1 seq 2 seq3 seq4 • Heuristic search . • builds up a final MSA by combining pair wise alignments beginning with the most similar pair and progressing to the most distantly related. • Stages: • The relationships between the sequences are represented as a tree, called a guide tree (pairwise alignment scores). • The MSA is built by adding the sequences sequentially to the growing MSA according to the guide tree.

15. Drawbacks • The primary problem is that when errors are made at any stage in growing the MSA, these errors are then propagated through to the final result.  Random/ iterative approaches are used • Performance is also particularly bad when all of the sequences in the set are rather distantly related.

16. System Architecture 4. Forking VMs / deleting VMs New VMs 2. Parallel transmission over Internet 3. Copy to HDFS AGT….CG AGT….CG Head Server (VM) AGT….CG New VMs AGT….CG AGT….CG ………. . . 5. Perform Alignment SEQUENCE FRAGMENTS 1. Create virtual environment 2. Split the sequences New VMs 6. Report the result SERVER SIDE HADOOP CLUSTER CLIENT SIDE VIRTUAL ENVIRONMENT

17. Map Task 3 Map Task 1 Map Task 2 D1,B3 D3,B1 D2,B2 D3,B2 K6,C3 K3,C3 K4,C3 K2,C2 K5,C2 K3,C2 Reduce Task 1 Reduce Task 2 Sort and Group (D2) K1,[C1] K2,[C1,C4] K3,[C1,C3] K4,[C4,C3] K5,[C4] K6,[C3] K1,I K2,I K3, I K4, I K5, I K6,I K1, I K2, I K3, I K5, I K6, I K1,[C6] K2,[C2] K3,[C2,C6] K5,[C2] K6,[C6] D1,B1 D2,B1 D1,B2 K1,C1 K2,C1 K3,C1 M R M M M M M R R R R R M R R R R R Sort and Group (D1) K6,C6 K3,C6 K1,C6 K5,C4 K2,C4 K4,C4 K5,C7 K6,C7 K4,C7 K4,C5 K1,C5 K6,C5 Map reduce Architecture

18. A single Combination – An illustration

19. S1= “AGTA”; A2=“ATA”; A3=“GAT” 1. ALIGNMENT OF SI & S2 2. ALIGNMENT OF A1SI & S3 SCORE: 4 A1S1:“AGTA”; A1S2:“A_TA” SCORE: -5 A2S1:“AG_TA”; A1S3:“_GAT_”

20. 3. ALIGNMENT OF A1S2 & A1S3 SCORE: -3 A2S2:“A _ _TA_”; A2S3:“ _GAT_ _”

21. Analysis ‘n’ – Number of Sequences ‘N’ – Average length of a sequence ‘k’ – Average number of blocks in a sequence ‘K’ – Size of 1 block

22. 2. Parallelised data trasfer ‘T’ – Time for sequence transfer serially & ‘k’ – block size T/k – Time for sequence transfer in parallel 3. Dynamic cluster creation Advantage: Computation power of remote cluster is optimal and not wasted Disadvantage: Time to set up the cluster

23. Experimental Setup • Core – 2 Duo processors – 2.8 GHz - 160GB HD, 2 GB RAM • LAN- 100 Mbps. • OS - RHEL v5 • Client virtual environment - 4 VMs • Server cluster - 5 machines • Hadoop DFS in fully distributed mode • OpenVZ was used for virtualization

24. Effect of parallel file transfer C1: Communication time from 3 client VMs to server without multithreading. C2: Communication time from 3 client VMs to the server with multithreading. T1: Total time for file transfer from client to server without multi threading T2: Total time for file transfer from client to server with multi threading

25. Time to start virtual machines Parallelised starting of VMs can be done to reduce time

26. 3 4 5 6 7 8 9 10 11 12 cluster performance wrt number of VMs 30 KB sequences with 2 KB splits – upto 5 sequences Number of sequences is less than 6, a five node hadoop cluster is sufficient.

27. Dynamic scaling up/down of clusters VMs instantiated based on number of Map-Reduce Tasks Dynamically number of tasks were checked up  New VMs started and tasks were reallocated Old VMs were destroyed if not used

28. Conclusion 1) Proposed MSA improves on the computation time and also maintains the accuracy. • Parallelism of sequence alignment in three levels. Hadoop data grids - Data and compute parallelism & scalability • Dynamic Programming - accuracy. 2) Complexity is reduced from O(Nn) to O[K2 * (n *(n-1)/2)] • Combining progressive and dynamic approaches. • Blocking in hadoop 3) Enhancements (using clouds for MSA) • Automatic configuration of the cloud environment based on the computational needs • Efficient upload of data into the HDFS by parallel transfer of sequence fragments over the Internet.

29. Acknowledgements The Research has been carried out as a result of PSG-Yahoo Research programme on Grid and Cloud computing. Sincere Thanks to 1) Dr R Rudramoorthy, Principal, PSG College of Techniology, Coimbatore. 2) Mr K V Chidambaran, Director, Grid and Cloud Systems Group, Yahoo, Bangalore

30. THANK YOU QUESTIONS?

31. REFERENCES • Apache, (2002), Hadoop Documentation, retrieved on September 20, 2009, fromhttp://hadoop.apache.org/core/docs/r0.17.2/. • Tahir, N., Imitaz, S. and Shaftab, A., “Parallel Needleman-Wunsch Algorithm for Grid”. retrieved on January 19, 2009 from http://www.gridbus.org/~alchemi/files/Parallel%20Needleman% 20Algo.pdf • Michael, C., (2009). “Cloud Burst: highly sensitive read mapping with MapReduce”, Bioinformatics, 25(11), 1363-1369. • Lee, T., “A genomic CluE for Cloud Computing”, retrieved on January 13, 2009 from http://www.eurekalert.org/pub_releases /2009-04/uom-agc042309.php • Yongli, H. and Shen, J., “Sequence analysis scale up and acceleration using Grid and Cloud Computing yield efficient analyses of HIV-1 variants and other viruses”, retrieved on February 15, 2009 from www.iscb.org /uploaded/css/43/12056.pdf. • Philip, P., Andres, L., Eyal, L. and Michael, B. “Adding the easy button to the cloud with SnowFlock and MPI”, in Proceedings of 3rd ACM workshop in system level virtualization for HPC (2009), 122-127.