MoBIoS

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• MoBIoS
• A Metric-space DBMS to Support Biological
  Discovery
• Presenter Enohi I. Ibekwe

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Overview

• MoBIoS Project
• Motivation
• The challenge
• Established similarity measures
• Metric-space distance measure
• Disk-based metric tree index
• MoBIoS as a DBMS
• Application of MoBIoS

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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.

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Motivation

• Develop a DBMS to power Biological Information
  System

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The Challenge

• Established biological model of similarity
  measure do not form a metrics.
• Scalable disk-based metric-indexes suffer from
  the Curse of dimensionality

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Established Similarity Measure (I)

• Sequence Homology
• Query Sequence
• Database of sequences
• Substitution Matrix (PAM / BLOSUM)
• Similarity Measure
• Global Sequence Alignment (Edit distance)
• Local Sequence Alignment (Most important)

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Established Similarity Measure (II)

• Local Sequence Alignment
• 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
• Main issue Result is a set of answer.
• A metric distance function must return a single
  value for each pair of argument

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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
• Query Sequence Sub strings of length q (q-grams)
• Database of sequences Metric indexed records of
  fixed length q (indexed q-grams) strings.
• Substitution Matrix (mPAM)
• Similarity Measure (distance measure)
• Local Alignments is computed from global
  alignment.

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Metric-space Distance measure (II)

• mPAM substitution Matrix
• Accepted Point Mutation Model.
• PAM calculates scores based on frequency in which
  individual pairs of amino acids substituted for
  each other.
• mPAM instead of calculating frequency of
  substitutions (PAM), computes expected time
  between substitution.
• mPAM has been validated.(Validation)

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Metric-space Distance measure (III)

• Computing Local Alignment from Global Alignment
  (Algorithm)
• Offline
• Divide database of sequence into sub strings
  (q-grams)
• Build metric-space index structure on q-grams
• Online
• Divide query sequence into sub strings (q-grams)
• Using global alignment as a distance function to
  match query q-grams.

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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)

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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)

• GH-tree Bi-direction
• M-Tree Bottom-up
• In practice, both M-Tree and GH-Tree scale
  linearly

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Disk-based metric-tree index (Searching)
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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

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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

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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)

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Application of MoBIoS (I)

• MS/MS Protein Identification
• Breakdown protein into fragments called peptide
  using a protease enzyme
• 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.

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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,
  accession_id)

• 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)

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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
• DS.ms_peak P and
• MPAM250(PS, DS.sequence, range2)

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BLAST vs MoBIoS

• MoBIoS
• 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
• 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

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MoBIoS Demo

• MoBIoS http//ccvweb.csres.utexas.edu9080/msfoun
  d/ccForm.jsp
• PDB http//www.rcsb.org/pdb/

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Conclusion

• Biological data is not random and very likely
  exhibit the intrinsic structure necessary for
  metric-space indexing to succeed.

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References

• http//www.cs.utexas.edu/users/mobios/Publications
  /miranker-mobios-final-03.pdf
• http//www.cs.utexas.edu/users/mobios/Publications
  /mao-bibe-03.pdf
• http//www.cs.utexas.edu/users/mobios/
• http//www.mckoi.com/database/

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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,
• Non-Negativity
• d(x,y) gt 0 for x y d(x,y) 0 for x y
• Symmetry
• d(x,y) d(y,x)
• 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 Smith-Waterman.
• 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)
• 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)
• 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) lt 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 R0UR1URw-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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