Bioinformatics Data Representation and Integration

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Bioinformatics Data Representation and Integration

• By
• Ngozi Oleleh

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Table of Contents

• Introduction to Bioinformatics
• Proteins and Sequences
• Bioinformatics Tools
• The databases
• Blast Functions
• Bioindexing
• Conclusion

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What is Bioinformatics

• Bioinformatics is the use of computers to study
  and handle biological Information
• Bioinformatics can be looked at as an integration
  of computer science and Biology to help enhance
  the study of biological data which has been
  proven to be very extensive
• The role of computer science in this
  Interdisciplinary is to store the data(via
  databases) for future Analysis via biological
  tools
• This fields study includes but is not limited to
  the study of genes, dna sequences and protein
  structures

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Protein and Sequences

• Biological proteins are made up of 20 amino acids
• Alanine - ala - A
• arginine - arg R
• asparagine - asn N
• aspartic acid - asp D
• cysteine - cys C
• glutamine - gln Q
• glutamic acid - glu - E
• glycine - gly G
• Histidine - his H
• isoleucine - ile I
• leucine - leu L
• lysine - lys K
• methionine - met M
• phenylalanine - phe F
• proline - pro P
• serine - ser S
• threonine - thr - T

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Proteins and Sequences

• Combination of these amino acids make up protein
  structures and sequences
• Pdb database contains numerous protein structures
  that are similar by sequence alignment of fold
  recognition.
• Bioinformatics studies difference and
  similarities of these protein structures based on
  sequence similarity
• A Sequence is a combination of amino acids.
• This sequences can contain biological data, that
  can be used to denote information about families
  of proteins

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Bioinformatic Tools

• Mage
• Used to display protein singular structures
• Rasmol
• Used to display protein 3d Structure
• LALIGN
• For pairwise Sequence Alignment
• ClustalW
• Used for Multiple Sequence Alignment
• Ammp
• Molecular Modeling
• Sequence Alignment Tools
• FASTA
• BLAST (will be looked at extensively)

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Biological Databases

• There are over 5000 public biological databases
• These databases contain genomic, proteomic and
  microarray data.
• This so called data is made up of sequence of
  genes or amino acids of proteins
• Biological databases have become very useful to
  scientists. It is important in understanding and
  explaining a host of biological phenomena from
  the structure of biomolecules and their
  interaction, to the whole metabolism of organisms
  and to understanding the evolution of species.

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• This knowledge helps facilitate the fight against
  diseases, assists in the development of
  medications and in discovering basic
  relationships amongst species in the history of
  life.
• The biological knowledge is distributed amongst
  many different general and specialized databases.
  This sometimes makes it difficult to ensure the
  consistency of information.
• Biological databases cross-reference other
  databases with accession numbers as one way of
  linking their related knowledge together.

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• Bioinformatics databases can be grouped into 2
  groups Generalized databases and Specialized
  databases
• Generalized databases
• Primary Sequence Databases (EMBL, Genebank,DDJB)
• Protein Sequence Databases(Swiss-prot,UniProt,
  UniRef)
• Carbohydrate Databases (CarbBank)
• 3d structure Databases (PDB, EBI-MSD,NDB)

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Specialized Databases

• Specialized databases
• Specialized Sequence database
• Genome databases
• Specialized Protein Sequence database
• Specialize Structure databases
• Microarray databases
• Main focus are the Generalized databases

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Primary Sequence Database

• Primary sequence databases
• EMBL (European Molecular Biology Laboratory
  nucleotide sequence database at EBI, Hinxton, UK)
• GenBank (at National Center for Biotechnology
  information, NCBI, Bethesda, MD, USA)
• DDBJ (DNA Data Bank Japan at CIB , Mishima,
  Japan)

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Protein Sequence Database

• Protein sequence databases
• SWISS-PROT (Swiss Institute of Bioinformatics,
  SIB, Geneva, CH)
• TrEMBL (Translated EMBL computer annotated
  protein sequence database at EBI, UK)
• PIR-PSD (PIR-International Protein Sequence
  Database, annotated protein database by PIR, MIPS
  and JIPID at NBRF, Georgetown University, USA)
• UniProt (Joined data from Swiss-Prot, TrEMBL and
  PIR)
• UniRef (UniProt NREF (Non-redundant REFerence)
  database at EBI, UK)
• IPI (International Protein Index human, rat and
  mouse proteome database at EBI, UK)

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Other Databases

• Carbohydrate databases
• CarbBank (Former complex carbohydrate structure
  database)
• 3D structure databases
• PDB (Protein Data Bank cured by RCSB, USA)
• EBI-MSD (Macromolecular Structure Database at
  EBI, UK )
• NDB (Nucleic Acid structure Database at Rutgers
  State University of New Jersey , USA)

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Blast

• Blast is a heuristic algorithm to detect
  sequence
• similarity and is optimized for speed. It is
  suitable
• for large scale analysis
• What blast does is to match a queried sequence
  to
• certain positions of database sequences

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Quick Diversion

• Blast Example
• Sequence to be queried
• TSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQ

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• Sequences producing significant alignments
  Score(Bits) E Value
• pdb2FXPA Chain A, Solution Structure Of The
  Sars-Coronaviru... 82.4 3e-17 pdb2BEZF Chain
  F, Structure Of A Proteolitically Resistant ...
  81.6 5e-17 pdb1WNCA Chain A, Crystal
  Structure Of The Sars-Cov Spike P... 77.8
  7e-16 pdb1WYYA Chain A, Post-Fusion Hairpin
  Conformation Of The S... 76.6 1e-15 pdb2BEQD
  Chain D, Structure Of A Proteolytically Resistant
  ... 69.7 2e-13 pdb1ZVAA Chain A, A
  Structure-Based Mechanism Of Sars Virus... 68.6
  5e-13 pdb1ZV7A Chain A, A Structure-Based
  Mechanism Of Sars Virus... 65.9 3e-12
  pdb1ZV8B Chain B, A Structure-Based Mechanism
  Of Sars Virus... 65.5 4e-12 pdb1WDGA Chain A,
  Crystal Structure Of Mhv Spike Protein Fu... 25.4
  4.7 pdb2A11A Chain A, Crystal Structure Of
  Nuclease Domain Of R... 24.3 9.1

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Blast Functions in Databases

• Blast is one of the most heavily used data
  analysis tools available, hence large scale data
  analysis need to supports BLAST functions.
• Blast Support is achieved by defining a set of
  user-defined functions that return BLAST results
  as a table.
• Many databases Support Blast Functions
• Blast 2 major functions are
• BLAST_MATCH
• BLAST_ALIGN

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The Blast Functions

• function BLASTP_MATCH (
• query_seq CLOB,
• seqdb_cursor REF CURSOR,
• subsequence_from NUMBER default 1,
• subsequence_to NUMBER default -1,
• filter_low_complexity BOOLEAN default false,
• mask_lower_case BOOLEAN default false,
• sub_matrix VARCHAR2 default BLOSUM62,
• expect_value NUMBER default 10,
• open_gap_cost NUMBER default 11,
• extend_gap_cost NUMBER default 1,
• word_size NUMBER default 3,
• x_dropoff NUMBER default 15,
• final_x_dropoff NUMBER default 25)
• return table of row (t_seq_id VARCHAR2, score
  NUMBER, expect NUMBER)

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Parameter Description

• query_seq The query sequence to search. A
  sequence is just lines of sequence data. Blank
  lines are not allowed in the middle of bare
  sequence input.
• seqdb_cursor The cursor parameter supplied by the
  user when calling the function. It should return
  two columns in its returning row, the sequence
  identifier and the sequence string.
• Subsequence from Start position of a region of
  the query sequence to be used for
• the search. The default is 1.
• Subsequence To End position of a region of the
  query sequence to be used for
• the search. If -1 is specified, the sequence
  length is taken as subsequence to. The default
  is -1.
• Filter_low_complexity TRUE or FALSE. If TRUE, the
  search masks off segments of the query sequence
  that have low compositional complexity. Filtering
  can eliminate statistically significant but
  biologically
• uninteresting regions, leaving the more
  biologically interesting regions of the query
  sequence available for specific matchingagainst
  database sequences. Filtering is only applied to
  the query sequence. The default value is FALSE.
• mask_lower_case TRUE or FALSE. If TRUE, you can
  specify a sequence in upper case characters as
  the query sequence and denote areas to be
  filtered out with lower case. This customizes
  what is filtered from the sequence. The default
  value is FALSE.

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• sub_matrix Specifies the substitution matrix used
  to assign a score for aligning any possible pair
  of residues. The different options are PAM30,
  PAM70, BLOSUM80, BLOSUM62, and BLOSUM45. The
  default is BLOSUM62.
• expect_value The statistical significance
  threshold for reporting matches against database
  sequences. The default value is 10. Specifying 0
  invokes default behavior.
• open_gap_cost The cost of opening a gap. The
  default value is 11. Specifying 0 invokes default
  behavior.
• extend_gap_cost The cost of extending a gap. The
  default value is 1. Specifying 0 invokes default
  behavior.
• word_size The word size used for dividing the
  query sequence into subsequences during the
  search. The default value is 3. Specifying 0
  invokes default behavior.
• x_dropoff Dropoff for BLAST extensions in bits.
  The default value is 15. Specifying 0 invokes
  default behavior.
• final_x_dropoff The final X dropoff value for
  gapped alignments in bits. The default value is
  25. Specifying 0 invokes default behavior.
• t_seq_id The sequence identifier of the returned
  match.
• score The score of the returned match.
• expect The expect value of the returned match.

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How the whole system Works

• Sequences that need to be searched are inserted
  into a query table
• INSERT INTO query_db VALUES (1,
  AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGT)

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How does it work

• Select T_SEQ_ID, score, EXPECT as evalue from
  TABLE(BLASTP_MATCH ( (select sequence from
  query_db), -- query_sequenceCURSOR(SELECT
  seq_id, seq_dataFROM swissprotWHERE organism
  'Homo sapiens (Human)'), -- seqdb_cursor 1, --
  subsequence_from-1, -- subsequence_to0, --
  FILTER_LOW_COMPLEXITY0, -- MASK_LOWER_CASE
  'BLOSUM62', -- SUB_MATRIX10, -- EXPECT_VALUE0,
  -- OPEN_GAP_COST0, -- EXTEND_GAP_COST0, --
  WORD_SIZE0, -- X_DROPOFF0)) --
  FINAL_X_DROPOFFt where t.score gt 25

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The Search Procedure

• SELECT t.t_seq_id, t.score, t.expect, p.name
• FROM PROT_DB p, TABLE(
• BLASTP_MATCH (
• (SELECT sequence FROM query_db WHERE sequence_id
  2),
• CURSOR(SELECT seq_id, sequence FROM PROT_DB),
• 1,
• -1,
• 0,
• 0,
• BLOSUM62,
• 10,
• 0,
• 0,
• 0,
• 0,
• 0)
• )t WHERE t.t_seq_id p.seq_id AND t.score gt 25
• ORDER BY t.expect

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Output Results

• SEQ_ID SCORE EVALUE
• -------- ---------- ----------
• P31946 205 5.8977E-18
• Q04917 198 3.8228E-17
• P31947 169 8.8130E-14
• P27348 198 3.8228E-17
• P58107 49 7.24297332

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The Databases and Why

• The ability to perform genome-wide and
  cross-genome data analysis can reduce time
  required for new biological discoveries
• Since traditional databases are not built to
  support location datatypes, researchers are
  forced to find ways in which these databases can
  manage biological information that will permit
  information to be queried with a Modern database
  system
• This research has led to a concept called
  Bioindexing

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Bioindexing

• An index in this construct is basically a way of
  providing a mapping between information entities.
• In a traditional database, an index is an
  auxiliary structure which speeds up the data
  retrieval process by providing a mapping between
  a record key and the physical disk address of the
  records containing the key
• Bioindexing provides similar functionality as a
  database index but also facilitates DATA
  INTEGRATION
• Biological features are generally attached to
  locations and locations are also the bases for
  maps(MAPS in this context is an association of
  features with a sequence alignment), alignment (
  relationships between two genomic sequence
  segments ) and other complex relationships.

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The Blast Database and Bioindexing

• Bioindexing is essentially an infrastructure for
  representing and managing biological knowledge in
  a large-scale database system using index
  constructs
• Bioindexing uses location datatype and BLAST
  JOINS to efficiently handle and query the large
  amount of data.
• Bioindexing is essentially a scheme for
  connecting and querying information with modern
  database systems WITH THE USE OF INDEXES

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Types of Indexing

• Intrinsic Indexing Indexable bioinformatics
  datatypes. Intrinsic indexing permits both the
  representation and management of biological
  mapping
• Extrinsic Indexing is basically an efficient
  way of data integration from different
  heterogeneous sources such as relational tables,
  xml files standard sequence formats and other
  sources.
• Extrinsic indexing concerns the functions and
  algorithms used to access and connect this
  information, even when it is not stored locally

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Location (How it is represented)

• Without proper abstraction, users have to
  implement their own codes to handle location
  operations
• A location consists of a sequence identifier and
  an interval range.
• Integer Interval are modeled in lower,upper
  structure
• Identifiers are character strings or accession
  numbers used to denote a particular sequence and
  interval range consists of a pair of positive
  integers used to denote the sub-range within the
  given sequence

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Complexity (Where Clauses ) if no location
DatatypesEst sequences being needed to be
grouped over consecutive overlapping EST
fragments

• SELECT DISTINCT A.id, A.lower, B.upper
• FROM ESTs AS A, ESTs AS B
• WHERE A.unigene_clusterid B.unigene_clusterid
• AND A.lower lt B.upper
• AND NOT EXISTS
• (SELECT
• FROM ESTs AS C
• WHERE C.unigene_clusterid A.unigene_clusterid
• AND A.lower lt C.lower AND C.lower lt B.upper
• AND NOT EXISTS
• (SELECT FROM ESTs AS D
• WHERE D.unigene_clusterid A.unigene_clusterid
• AND D.lower lt C.lower AND C.lower lt D.upper))
• AND NOT EXISTS
• (SELECT
• FROM ESTs AS E
• WHERE E.unigene_clusterid A.unigene_clusterid
• AND ((E.lower lt A.lower AND A.lower ltE.upper) OR

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Location Datatype

• A straightforward representation of a location
  would be a sequence identifier as a character
  string and the location interval as (start, end)
  pair of integers.
• There are other possible representations such as
  integer codes for sequence identifiers and or a
  (start,length) interval representation
• Most databases use the sequence identifier, and
  location (start, end ) pair of integers..
  WHY..because of Simplicity

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Simplicity using Location DatatypeCreation and
Insertion

• CREATE TABLE features ( location loc, description
  text)
• -- The Prader-Willi/Angelman syndrome region on
  chromosome 15
• INSERT INTO features VALUES ( 'NG_0026901..755217
  ', 'Prader-Willi/Angelman syndrome region' )
• INSERT INTO features VALUES ( 'NG_0026901..174707
  ', 'AC090602.16' )
• INSERT INTO features VALUES ( 'NG_002690174707..3
  24834', 'AC124312.5' )
• INSERT INTO features VALUES ( 'NG_002690324835..4
  78258', 'AC124303.5' )
• INSERT INTO features VALUES ( 'NG_002690478259..6
  06120', 'AC100774.2' )
• INSERT INTO features VALUES ( 'NG_002690606121..7
  55217', 'AC124997.4' )

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• The introduction of location datatype not only
  provides a natural and intuitive way to represent
  biological information, but also boosts system
  performance.
• Additional performance increase could be achieved
  by supporting the location index scheme.
• Supports for indexing schemes in traditional
  relational database systems are very limited and
  inflexible.
• They are only limited to a few well-known index
  structures, such as B-tree, Hash and R-tree and
  could be used for a limited set of native
  data-types for (in)equality and range queries.

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• Essentially there are operation and functions
  supported in the location datatype.
• A major proportion of these functions are related
  to interval operations.
• More than 30 interval operations are defined,
  including Allen's interval logic 15 (which
  includes after, before, contains, during, equals,
  overlaps, overlapped by,
• finishes, finished by, meets, met by, starts and
  started by).
• Optimization information (such as regarding
  ordering, commutativity or negation) is also
  provided to permit optimization of important
  operations like merge-join, hash-join or general
  theta-join.

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Why location datatype is Needed

• Here is a simple example to demonstrate the power
  of location datatype support. This example shows
  a session that painfully attempts to locate
  alternatively spliced exon intervals which
  intersect with known homology intervals and
  associate them with known protein features from
  the Pfam and Swissprot databases.

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Complexity without locations

• CREATE TABLE alt_splice_homology_map AS
• SELECT o., d.swiss_id, d.query_start,
  d.query_end,
• d.hit_start(o.seq_start-d.query_start)/3,
• d.hit_start(o.seq_end-d.query_start)/3,
• FROM alt_splice_exon_obs o, alt_splice_homology
  d
• WHERE o.ug_id d.ug_id
• AND o.seq_start gt d.query_start
• AND o.seq_start lt d.query_end
• AND d.e_value lt 0.01
• GROUP BY o.ug_id, o.seq_start
• SELECT o., f.type, f.start, f.end
• FROM alt_splice_homology_map o, swiss_feature f
• WHERE o.swiss_idf.swiss_id
• AND o.hit_end gt f.start
• AND o.hit_end lt f.end

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Simplicity using locations

• CREATE TABLE alt_splice_homology_map AS
• SELECT o., d.location,
• range_start(d.query)(o.location-range_start(d.hit
  ))/3
• FROM alt_splice_exon_obs o, alt_splice_homology d
• WHERE o.location _at_ d.location -- contained
• AND d.e_value lt 0.01
• GROUP BY o
• SELECT o., f.type, f.location
• FROM alt_splice_homology_map o, swiss_feature f
• WHERE o.location lt f.location -- left overlap

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Location Support

• Supporting location indexing in a traditional
  database implies the need to support interval
  indexing.
• BUT, interval indexing is not supported in
  traditional databases and standard join
  operations could not handle intervals
  efficiently, this has led to extensive research
  for interval indexing.
• Here lies the need for a concept called GIST

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GIST

• Is an efficient solution handle the problem of
  ineffective interval indexing in traditional
  database
• Gist is basically a balanced search tree in which
  keys are maintained in a hierarchical manner. The
  search keys used in gist may be any arbitrary
  predicate, but this predicate must hold true for
  the data searched below a key.
• Gist searches by traversing the entire tree in a
  dept-first search manner. If the query predicate
  is consistent with a given search key, Gist will
  continue to search the subtree below the key

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Gist Implementation

• Gist is implemented using bounding intervals that
  covers the range of
• Identifier integers (id_lower,id_upper)
• And
• Intervals in the subtree (lower,upper)
• Under Gist architecture interval predicates such
  as such as left, right overlap,
  overleft,overright, contains, contained and equal
  are all supported

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What gist location does
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Conclusion

• Bioinformatics databases are being modeled and
  queried using function(as seen in oracle and ibm
  DB2)
• An efficient way of modeling these databases are
  seen using bioindexing (as seen in postgre- sql
  database)
• The use of an index structure as seen in
  Bioindexing, where a location is modeled using a
  (DFS) tree structure leads to less complexity.
• This location index structure leads to an faster
  searching of the databases
• This concept of speed is very important in
  bioinformatics
• Using a gist architecture, lead to less complex
  queries and a more confined search sector for
  query information.

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References

• The Index as a First-Class Construct in
  Relational Database Systems
• D. Stott Parker, Edwin Mach
• Algorithms and Databases in Bioinformatics
  Towards a Proteomic Ontology
• Mario Cannataro, Pietro Hiram Guzzi, Tommaso
  Mazza, Giuseppe Tradigo and Pierangelo Veltri
• Oracle Data Mining
• Mobile Access to Biological Databases on the
  Internet
• Pentti Riikonen, Jorma Boberg, Tapio Salakoski,
  and Mauno Vihinen
• Utilizing Multiple Bioinformatics Information
  Sources
• An XML Database Approach
• Raymond K. Wong William M. Shui
• Support for BioIndexing in BLASTgres
• Ruey-Lung Hsiao, D. Stott Parker, and Hung-chih
  Yang