Parallel Computation in Biological Sequence Analysis

1. Parallel Computation in Biological Sequence Analysis Xue Wu CMSC 838 Presentation

2. Motivation • Scanning and analyzing biological sequences are common and repeated tasks in molecular biology • Homologous sequence searching • Based on pairwise alignment • Task is to find similarities between a particular query sequence and all the sequences of a biosequence databank • Multiple sequence alignment • Simultaneous alignment of three or more nucleotide or amino acid sequences • Problems with sequential solution • With the exponential growth of the biosequence banks, homologous sequence searching becomes time consuming • The automatic generation of an accurate multiple alignment is computationally expensive • Parallel solution can reduce computation time and provide more accurate result CMSC 838T – Presentation

3. Talk Overview • Overview of talk • Motivation • Techniques and Evaluations • Similarity Sequence Searching • Multiple Sequence Alignment • Observations CMSC 838T – Presentation

4. Techniques – similarity sequence searching • Two main parallel methods to search sequence database • Fine grain approach for SIMD parallel computer • Parallelize the comparison algorithm itself • All processors cooperate to determine the similarity score • Coarser grain approach for MIMD parallel computer • Parallelize the database searching • Each processor performs a selected number of comparison • method used in the paper • Parallelize Similarity Searching – coarser grain approach • Workload balancing is the key point for better parallelism • Partition database, combine results from sequential search for each database requires equal-sized pieces of database for load balance • Percentage of Load ImBalance (PLIB) as metric for load imbalance CMSC 838T – Presentation

5. Techniques – similarity sequence searching • Splitting up database • Unsorted portion method – first load balancing technique • Partition the database into a number of portions • Portion_size = database_size / processors_number • If sequence assignment causes sum of sequence lengths in portion P exceed ideal size by more than X percent, reassign the sequence to portion P+1 • Low communication overhead, but possibly high PLIB • Sorted portion method – Master-worker method • Sequences are sorted in decreasing length order to minimize PLIB • The master processor distributes the sequences to the worker processors dynamically • Low PLIB, but high communication overhead CMSC 838T – Presentation

6. Techniques – similarity sequence searching • Proposed bucket method • Statically apply sorted portion method • Algorithm • Sequences in the database are sorted in decreasing length order • Starting from the longest-length sequence, place the sequences in N buckets. For each sequence, • Find the sum of the sequences length in each bucket • Find the bucket with the smallest sum value • Place the sequence in the bucket • In the case of a tie, the smallest numbered bucket is selected • Each of the N processors performs sequence search in its own bucket • If only N/n processors are used, each processor searches n bucket CMSC 838T – Presentation

7. Techniques – similarity sequence searching • Evaluation and comparison • Comparison of Bucket and Portion method • Comparison of Bucket and Master-worker method • Algorithms are implemented on the Intel iPSC/860 • Preprocessing is performed on SPARC station 2 • Data source is GenBank (release 86.0) • Preprocessing overhead is added for Bucket method CMSC 838T – Presentation

8. Techniques – similarity sequence searching • Evaluation and comparison continued • Conclusions • In all tested cases, proposed Bucket method has • Lower PLIB than Portion method • Higher speedup than master-worker method • Bucket method has obvious advantage when • Sequences length is relatively small • Processing with large number of processors CMSC 838T – Presentation

9. Accept Calculate alignment Reject Techniques – multiple sequence alignment • Sequential Berger-Munson algorithm CMSC 838T – Presentation

10. Techniques – multiple sequence alignment • Sequential Berger-Munson algorithm • Applied randomized techniques with optimization to iteratively improve the multiple sequence alignment • Description • Randomly partition the input sequences into two groups • Align two groups of sequences instead of individual sequence with alignment score calculated by • If the new alignment score is higher than the previous one, the alignment is accepted and the gaps are inserted into the sequences accordingly. • The modified or unmodified alignment is used as the input for the next iteration. The process is stopped after q consecutive iterations of rejection. CMSC 838T – Presentation

11. Sequential Iteration number 1 2 3 4 5 6 7… Decision Sequence AAAAARAAARRARRRARRRARRRARRRRR… 13 21 10 17 25 8 1 4 6 9 3 0 2 5 14 11 10 18 22 26 3 9 2 4 1 6 5 7 19 15 12 27 11 10 23 5 8 3 7 4 6 2 11 20 16 24 13 12 28 3 8 7 6 5 4 9 Techniques – multiple sequence alignment • Parallel Berger-Munson algorithm with speculative computation • Consecutive sequence of rejected iterations are not dependent on each other and can be done in parallel CMSC 838T – Presentation

12. Techniques – multiple sequence alignment • Evaluation • Method: Improve the alignment generated by experts and other program (CLUSTALV) • Data Source • Three different groups of immunoglobulin sequences from Kabat Database (Beta Release 5.0) • The average sequence lengths of three groups are similar • The number of sequences are different, which in each group is as twice as the previous group CMSC 838T – Presentation

13. Techniques – multiple sequence alignment • Evaluation continued • Alignment score comparison • Apparently, Berger-Munson method provides more accurate alignments with sacrifice of computation time, which is not ignorable CMSC 838T – Presentation

14. Techniques – multiple sequence alignment • Parallel Algorithm Speedup factor • Conclusion • The original iterative method is a good tool for improving alignment results • With the parallel speculative computation technique, it can • Increase the alignment score • Reduce the computation time • Can achieve higher speedup factors when • Processing large_sized sequence group • Processing sequences with high alignment score • Cannot be compared with the previous algorithm by Ishikawa et al. CMSC 838T – Presentation

15. Observations • Similarity Sequence Searching • With the increasing size of biosequence database and growth of computation power, coarse grain parallelism for sequence searching is more simple and effective • Time required for processing any given sequence depends not only on the length of sequence, but also on • The composition of the sequence • CPU power and CPU availability • So dynamic load balancing is still necessary. • To minimize communication and scheduling overhead, • Distributing sequences by fixed/variable size block • Applying buffering strategy to reduce data starvation and shadow scheduling latency • Multiple Sequence Alignment • With the increasing of computation power, parallelizing single multiple sequence Alignment is not necessary. However, using parallelism to increase the alignment accuracy is still attractive. • Using computation time to exchange for alignment accuracy CMSC 838T – Presentation

16. Thank you!