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MoBIoS
A Metric-space DBMS to Support
Biological Discovery
Presenter: Enohi I. Ibekwe
Overview
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MoBIoS Project
Motivation
The challenge
Established similarity measures
Metric-space distance measure
Disk-based metric tree index
MoBIoS as a DBMS
Application of MoBIoS
MoBIoS Project
• Molecular Biological Information System
• Project at UT-Austin center for computational biology
and bioinformatics.
• DBMS based on metric-space indexing techniques,
object-relational model of genomic and proteomic data
types and a database query language that embodies the
semantics of genomic and proteomic data.
Motivation
Develop a DBMS to power Biological
Information System
The Challenge
• Established biological model of similarity
measure do not form a metrics.
• Scalable disk-based metric-indexes suffer
from the Curse of dimensionality
Established Similarity Measure
(I)
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Sequence Homology
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Query Sequence
Database of sequences
Substitution Matrix (PAM / BLOSUM)
Similarity Measure
– Global Sequence Alignment (Edit distance)
– Local Sequence Alignment (Most important)
Established Similarity Measure
(II)
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Local Sequence Alignment
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A local sequence alignment query asks, given a query
sequence S, a database of sequences T and a
similarity matrix corresponding to an evolutionary
model, return all subsequences of T that are
sufficiently similar to a subsequence of S
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Main issue: Result is a set of answer.
• A metric distance function must return a single
value for each pair of argument
Established Similarity Measure
(III)
• Global Sequence Alignment
– Given an alphabet A , a similarity substitution matrix M
corresponding to an evolutionary model, the global
sequence alignment for two sequences s and t is to find
a strings a and b which are obtained from s and t
respectively by inserting spaces either into or at the
ends of s and t and whose score computed using M is at
a maximum (Similarity measure) over all pairs of such
strings obtained from s and t. (example)
– Issue: Result maybe negative since substitution matrix
is based on log-odd probability. Similarity measure
favors greater positive number.
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Metric-space Distance measure
(I)
Homology Search
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Query Sequence: Sub strings of length q (q-grams)
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Database of sequences: Metric indexed records of
fixed length q (indexed q-grams) strings.
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Substitution Matrix (mPAM)
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Similarity Measure (distance measure)
– Local Alignments is computed from global
alignment.
Metric-space Distance measure
(II)
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mPAM substitution Matrix
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Accepted Point Mutation Model.
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PAM calculates scores based on frequency in which
individual pairs of amino acids substituted for each
other.
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mPAM instead of calculating frequency of
substitutions (PAM), computes expected time
between substitution.
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mPAM has been validated.(Validation)
Metric-space Distance measure
(III)
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Computing Local Alignment from Global
Alignment (Algorithm)
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Offline
1. Divide database of sequence into sub strings (qgrams)
2. Build metric-space index structure on q-grams
– Online
1. Divide query sequence into sub strings (q-grams)
2. Using global alignment as a distance function to
match query q-grams.
Disk-based metric-tree index
• Phases
• Initialization
• Searching
• Query performance metric
• Number of disk I/O ( nodes visited)
• Number of distance computation
• Options Exploited
• M-Tree
• Generalized Hyper plane tree
• MVP-Tree (optimal)
Disk-based metric-tree index
(initialization)
• M-Tree initialization
– Best case : O(nlogn);
– worst case: O(n3)
• Generalized Hyper
plane (GH-Tree)
initialization
– Best case : O(nlogn);
– worst case: O(n2)
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GH-tree: Bi-direction
M-Tree: Bottom-up
In practice, both M-Tree and GHTree scale linearly
Disk-based metric-tree index
(Searching)
MoBIoS as a DBMS (I)
• Mckoi (Java RDBMS).
– Plus metric-space indexing
– Plus Biological data types
– Plus biological semantics
• Life science data store
– Biological sequence data
– Mass-spectrometry protein
signature
MoBIoS as a DBMS (III)
• Language Extension
– M-SQL
• Data type Extension
– Data type for Sequences (DNA,RNA,peptide)
– Data type for Mass spectrum
• Semantics Extension
– Subsequence Operators
– Local alignment
MoBIoS as a DBMS (IV)
• Semantics Extension
– Similarity (metric distance) between data types
• mPAM250
• Cosine distance
• Lk norms
• Keys Extension
– Primary key (metrickey)
– Index (metric)
Application of MoBIoS (I)
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MS/MS Protein Identification
1. Breakdown protein into fragments called
peptide using a protease enzyme
2. Identify protein by using a mass-spectrometer
to measure the mass-charge ratio of the
fragments and comparing the experiment result
to a database of precomputed spectra.
Application of MoBIoS(II)
M-SQL Solution
Create table
protein_sequences
(accesion_id int,
sequence peptide,
primary
metrickey(sequence,
mPAM250);
Create table
digested_sequences
(accession_id int,
fragment peptide,
enzyme varchar,
ms_peak int, primary
key(enzyme,
Create index
fragment_sequence on
digested_sequences
(fragment)
metric(mPAM250);
Create table
mass_spectra
(accession_id int,
enzyme varchar,
spectrum spectrum,
primary
metrickey(spectrum,
cosine_distance);
Application of MoBIoS(III)
• M-SQL Solution
SELECT Prot.accesion_id,
Prot.sequence
FROM protein_sequences Prot,
digested_sequences DS,mass_spectra
MS
WHERE
MS.enzyme = DS.enzyme = E and
Cosine_Distance(S, MS.spectrum,
range1) and
DS.accession_id = MS.accession_id
= Prot.accesion_id and
BLAST vs MoBIoS
MoBIoS
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Molecular Biological
Information System
DBMS specialized for storage,
retrieval and mining of biological
data
Sequence Database and query
sequence is divided into q-grams
and Database is indexed offline.
BLAST
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Basic Local Alignment Search
Tool
Utility specialized for retrieval
and mining of biological data
outside a database
Only query sequence is divide
and hot-point index is done at
query time
MoBIoS Demo
• MoBIoS:
http://ccvweb.csres.utexas.edu:9080/msfoun
d/ccForm.jsp
• PDB : http://www.rcsb.org/pdb/
Conclusion
• Biological data is not random and very
likely exhibit the intrinsic structure
necessary for metric-space indexing to
succeed.
References
• http://www.cs.utexas.edu/users/mobios/Publicat
ions/miranker-mobios-final-03.pdf
• http://www.cs.utexas.edu/users/mobios/Publicat
ions/mao-bibe-03.pdf
• http://www.cs.utexas.edu/users/mobios/
• http://www.mckoi.com/database/
Appendix
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Appendix I- Metric
A metric-space is a set of objects S, with a
distance function d, such that given any
three objects x, y, z,
1. Non-Negativity
d(x,y) > 0 for x = y; d(x,y) = 0 for x = y
2. Symmetry
d(x,y) = d(y,x)
3. Triangular inequality
d(x,y) + d(y,z) = d(x,y)
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Appendix II - Sequence
• 2 RNA sequences from a DNA strand.
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Appendix III - PAM
Percent Accepted Mutation(PAM)
A PAM(x) substitution matrix is a look-up table in
which scores for each amino acid substitution
have been calculated based on the frequency of
that substitution in closely related proteins that
have experienced a certain amount (x) of
evolutionary divergence. (e.g PAM250)
A unit to quantify the amount of evolutionary change in a protein sequence. Based
on log-odd probability.
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Appendix IV – PAM250
• At this evolutionary distance (250 substitutions per hundred
residues)
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Appendix V - BLOSUM
Blocks Substitution Matrix (BLOSUM)
A substitution matrix in which scores for each
position are derived from observations of the
frequencies of substitutions in blocks of local
alignments in related ( e.g BLOSUM62)
A unit to quantify the amount of evolutionary change in a protein sequence. Based
on log-odd probability
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Appendix VI – BLOSUM62
• BLOSUM62 matrix is calculated from protein blocks such
that if two sequences are more than 62% identical
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Appendix VII – mPAM250
• Expected time based on 250 PAM distance as a unit.
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Appendix VIII – mPAM
Validation
• Based on benchmark
query set by SmithWaterman.
• Graph shows ROC50
values (Receiver
Operating Characteristics)
• Negative x- axis indicate
mPAM has better
performance
Difference between ROC50 values
using mPAM and PAM250
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Appendix IX - Distance measure
Global Sequence Alignment
Given an alphabet A , a similarity substitution matrix
M corresponding to an evolutionary model, the global
sequence alignment for two sequences s and t is to find
a strings a and b which are obtained from s and t
respectively by inserting spaces either into or at the
ends of s and t and whose score computed using M is
at a maximum (Similarity measure) or minimum
(distance measure) over all pairs of such strings
obtained from s and t.
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Appendix X – Homology Search
Build Index Structure(Offline)
1.
2.
Divide the database sequences into a set of overlapping sub
strings of length q (q-grams) with step size 1.
Build a metric-space index D based on global alignment to
support constant time lookup of exact match.
Homology Search Query (Online)
1.
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Divide the query sequence W into overlapping sub string , F
= {wi | i =0..| W |-q }, of length q with step size 1.
For each wi in F, run range query Q(wi, r) against database D
to find a set of matching q-grams, Ri = f i,j | d( f i,j , wi) <= r,
f i,j E D wi E F }, where d is the distance function.
Using a greedy heuristic algorithm to extend and chain all
fragments in R0UR1U…Rw-t to deduce the result of
homology search based on local alignment for query W
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Appendix XI - GSA
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