Parallel Computation in Biological Sequence Analysis

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Parallel Computation in Biological Sequence
Analysis

• Xue Wu
• CMSC 838 Presentation

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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

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Talk Overview

• Overview of talk
• Motivation
• Techniques and Evaluations
• Similarity Sequence Searching
• Multiple Sequence Alignment
• Observations

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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

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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
  P1
• 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

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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

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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

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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

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Techniques multiple sequence alignment

• Sequential Berger-Munson algorithm

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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.

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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

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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

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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

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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.

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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

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Thank you!