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
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Introduction
Indexing strategies
Suffix trees
New Construction Algorithm
Query
Experiment and Results
Conclusion
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)
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.
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.
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
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…
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 edgelabels on the path from the root to leaf i
exactly spells out the suffix of S that starts at
position i
Suffix Trees
Suffix tree is a compressed digital
(suffix) trie
Suffix tree building
Suffices of mississippi:
1
mississippi
2
ississippi
3
ssissippi
4
sissippi
5
issippi
6
ssippi
7
sippi
8
ippi
9
ppi
10 pi
11 i
root
i
s
s
i
s
s
i
p
p
i
i
Result suffix tree building
p
root
s
i
i
9
11
i
ssi
10
si
8
4
1
2
5
6
3
7
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
Suffix Trees
 Construction
 Suffix link
Complexity O(n)
Ukkonen’s Method
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
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.
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
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.
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.
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)
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
New Construction Algorithm
 The actual algorithm [Pseudo code]
New Construction Algorithm
1
2
3
4
5
root
ANA$
NA$
A$
$
Tree creation for ANA$
root
$
ANA$
A
NA$
2
NA$
2
3
$
4
5
New Construction Algorithm
Original tree (Ukkonen)
left index
right index
suffix number
Modified Node
left index
child
child
sib
suffix link
sib
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
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, $, *
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
Experiment Results
Exact String matching using 263Mbp of human DNA
Queries sent in batches using warm storage
Experiment Results
Cold Storage
Experiment Results
Further Work
 Improvements to the tree representation
and incremental construction algorithm.
 Investigation of the interaction between
approximate matching algorithms and diskbased suffix trees.
 Investigation of alternative persistent
storage solutions.
 Integration of the algorithms with biological
research tools and usability studies.
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.