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Database Index to Large Biological Sequences

Database Index to Large Biological Sequences. Ela Hunt, Malcolm P. Atkinson, and Robert W. Irving Proceedings of the 27th VLDB Conference,2001 Presented by Raghav & Balaji. Indexing Large Biological Sequences. Introduction Indexing strategies Suffix trees New Construction Algorithm Query

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Database Index to Large Biological Sequences

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  1. Database Index to Large Biological Sequences Ela Hunt, Malcolm P. Atkinson, and Robert W. Irving Proceedings of the 27th VLDB Conference,2001 Presented by Raghav & Balaji

  2. Indexing Large Biological Sequences • Introduction • Indexing strategies • Suffix trees • New Construction Algorithm • Query • Experiment and Results • Conclusion

  3. Introduction • What's a DNA? • A, C, G, T (A with T, C with G)‏ • Base pair • Gbp (Giga base pairs)‏ • Mammalian genomes – 3Gbp • What is the challenge in indexing DNA? • Large Size and no definite pattern • Searching genetic DNA sequences • Sequentially scanning and filtering approach (BLAST, FASTA)

  4. Introduction • Rise in volume of data and demand for searches by researchers accelerated the need for better searches using indexes. • New Sequences will be revealed as improved sequencing techniques are developed. • Determining DNA sequences is useful in studying fundamental biological processes, as well as in forensic research.

  5. Indexing Strategies Considered • Inverted files • Not suitable since DNA cannot be broken into words. • B-tree • Same as above • Q-grams • Cannot deliver matches that have low similarity to the query. • Most of the techniques are infeasible.

  6. Indexing Strategies Considered • Suffix Trees • Ideal Choice for this type of indexing. • Suffix trees on disk could only be built for small sequences. • “Memory Bottleneck”. • Suffix tree storage optimization • Reduce the RAM required to around 13 bytes per character indexed • Not test on disk

  7. Indexing Strategies Considered • Approach to searching genetic DNA sequences using an adaptation of the suffix tree. • Build suffix tree on disk for arbitrarily large sequences • New query process strategies. • Alternative data structures • Q-grams, Suffix array, String B tree…

  8. Suffix Trees • Suffix tree - compressed digital trie. • A suffix tree is a rooted directed tree with m leaves, where m is the length S (the database string) • For any leaf i, the concatenation of the edge-labels on the path from the root to leaf i exactly spells out the suffix of S that starts at position i

  9. Suffix Trees Suffix tree is a compressed digital (suffix) trie

  10. Suffix tree building root p p i m i s s i s s i p p i Suffices of mississippi: • mississippi • ississippi • ssissippi • sissippi • issippi • ssippi • sippi • ippi 9 ppi 10 pi 11 i s s i s s i p p i i i s s i s s i p p i i s s i p p i p p i p p i p p i p p i

  11. Result suffix tree building root p m i s s i s s i p p i p i s i i 9 11 10 p p i i ssi si s s i p p i s s i p p i 8 s s i p p i p p i p p i p p i 4 1 5 6 2 3 7

  12. Suffix Trees • Suffix Links: • A necessary implementation trick to achieve a linear time and space bound during building the tree • A suffix link is: a pointer from an internal node xS to another internal node S where x is a arbitrary character and S is a possibly empty substring

  13. Suffix Trees • Construction • Suffix link Complexity O(n) Ukkonen’s Method

  14. Suffix Trees • General applications of Suffix trees • Find all occurrences of q as a substring of S • Longest substring common to a set T of strings • Find the longest palindrome in S

  15. Suffix Trees • Analysis of Suffix Link Based Algorithm • Build the tree incrementally, check pointing the tree after each portion has been attempted. • 2 distinct traversal patterns exist both of which are used during construction. • Very long construction time. • These effects combine to limit the size of the tree that can be constructed and stored on disk to the available main memory.

  16. Suffix Trees • Using Suffix link based algorithm, it was observed that checkpointing trees indexing more than 21Mbp was not possible using 1.8GB of main memory. • Reasons being • Object header size increases

  17. New Construction Algorithm • Difficulties of traditional suffix tree construction: • Memory bottleneck • Necessity of random access • New conception • To abandon the use of suffix links • To perform multiple passes over the sequence, constructing the suffix tree for a sub range of suffixes at each pass.

  18. New Construction Algorithm • Removing Suffix link means that the construction of a new partition does not modify previously checkpointed partitions of the tree. • Using multiple passes, it means that it is not necessary to access or update previously checkpointed partitions. • i.e. Data structure for the complete partitions can be evicted from the main memory and will not be faulted back during the rest of the tree’s construction.

  19. New Construction Algorithm • Partition concept: • Build multiple suffix tree that fit in memory(AC, AT or AG fall into different partitions) • Base on the prefixes of each suffix • Use a sliding window of length l. • Form a string s1 of window length, l. • Scan the string and count the number of occurrances of s1. • Use a bin packing technique to pack (s1, #occurrances)

  20. New Construction Algorithm • Partition technology: • Assumption:tree is uniformly populated. • Prefix code(Pi): • Suffixes that are indexed during the jth pass of the sequence have jr  Pi  (j+1)r

  21. New Construction Algorithm • The actual algorithm [Pseudo code]

  22. New Construction Algorithm Tree creation for ANA$ 1 root 2 ANA$ 3 NA$ 4 A$ 5 $ root $ ANA$ A NA$ 2 3 5 NA$ $ 2 4

  23. left index child sib New Construction Algorithm Original tree (Ukkonen) Modified Node left index right index suffix number child sib suffix link

  24. Query • Only exact pattern matching. • One query involves one partial traversal. • Complexity of suffix tree search: O(k+m); • k-query length, m-no of matches in the index. • Queries of length q bring back 1/(a^q) fraction of the whole tree where a = size of the active alphabet i.e. 4 (A,C,G,T). • New query strategies: • Short query: serial scan of the sequence • Longer query: using index structure • Threshold: 10 to 12 letters

  25. Experiment and Results • Develop and experiment platform: • Software: PJama, JAVA 1.3 & Solaris 7 OS • Hardware: Enterprise 450 with 2GB RAM • Test data • 6 single chromosomes of worm C. elegans(20.5Mbp max. length) • Human chromosomes 21,22, and 1(280Mbp) • Alphabets • A, C, G, T, $, *

  26. Experiment and Results • Trees with suffix link: (use 20.5Mbp DNA) • Construct in memory: 7 mins • Construct in disk: 34 hours • Trees without suffix link: (263Mbp DNA) • 19 hours

  27. Experiment Results Exact String matching using 263Mbp of human DNA Queries sent in batches using warm storage

  28. Experiment Results Cold Storage

  29. Experiment Results

  30. Further Work • Improvements to the tree representation and incremental construction algorithm. • Investigation of the interaction between approximate matching algorithms and disk-based suffix trees. • Investigation of alternative persistent storage solutions. • Integration of the algorithms with biological research tools and usability studies.

  31. Conclusion • Present an approach to searching genetic DNA sequences using an adaptation of the suffix tree data structure. • Allow to build suffix trees on disk for arbitrarily large sequences. • Open up the perspective of building suffix trees in parallel, and the simplicity of this approach can make suffix trees more popular.

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