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Comparison of Data Access and
Integration Technologies in the Life
Science Domain
Dr Richard Sinnott
Technical Director National e-Science Centre
|||
Deputy Director Technical Bioinformatics
Research Centre
University of Glasgow
Derek Houghton
Database Manager
Human Genetics Unit
Medical Research Council
Edinburgh
[email protected]
[email protected]
19th September 2005
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Life Sciences and Grids
Extensive Research Community
>1000 per research university
Extensive Applications
Many people care about them

Health, Food, Environment, …
Interacts with many disciplines
Physics, Chemistry, Maths/Statistics, Nano-engineering, …
Huge and expanding number of databases relevant to
bioinformatics community
Heterogeneity, Interdependence, Complexity, Change, Dirty…
Linking using in co-ordinated, secure manner full of open
issues to be addressed
Compute demands growing as more in-silico research
undertaken
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Database Growth
PDB Content Growth
•DBs growing exponentially!!!
•Biobliographic (MedLine, …)
•Amino Acid Seq (SWISS-PROT, …)
•3D Molecular Structure (PDB, …)
•Nucleotide Seq (GenBank, EMBL, …)
•Biochemical Pathways (KEGG, WIT…)
•Molecular Classifications (SCOP, CATH,…)
•Motif Libraries (PROSITE, Blocks, …)
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Distributed and Heterogeneous data
Structure
Sequence
LPSYVDWRSA
ECGGCWAFSA
TSGSLISLSE
NTRGCDGGYI
GGINTEENYP
Function
GAVVDIKSQG
IATVEGINKI
QELIDCGRTQ
TDGFQFIIND
YTAQDGDCDV
Gene expression
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Morphology
More genomes …...
Yersinia
pestis
Arabidopsis
thaliana
Buchnerasp.
APS
Caenorhabitis Campylobacter Chlamydia
elegans
jejuni
pneumoniae
Helicobacter Mycobacterium
pylori
leprae
rat
mouse
Aquifex
aeolicus
Vibrio
cholerae
Archaeoglobus Borrelia
Mycobacterium
fulgidus
burgorferi
tuberculosis
Drosophila
melanogaster
Escherichia Thermoplasma
coli
acidophilum
Neisseria
Plasmodium Pseudomonas Ureaplasma
meningitidis falciparum
aeruginosa urealyticum
Z2491
Rickettsia
Saccharomyces Salmonella
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cerevisiae
enterica
Bacillus
subtilis
Thermotoga
maritima
Xylella
fastidiosa
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+ links to plant/crops,
environmental, health, …
information sources
Populations
Organisms
Physiology
Tissues
Protein-protein interaction (pathways)
Protein Structures
Gene expressions
Nucleotide structures
Systems Biology
Is Grid the Answer?
Some key problems to be addressed
Tools that simplify access to and usage of data

Internet hopping is not ideal!
Tools that simplify access to and usage of large scale HPC facilities

qsub [-a date_time] [-A account_string] [-c interval] [-C directive_prefix] [-e path] [-h]
[-I] [-j join] [-k keep] [-l resource_list] [-m mail_options] [-M user_list] [-N name] [-o path]
[-p priority] [-q destination] [-r c] [-S path_list] [-u user_list] [-v variable_list] [-V] [-W
additional_attributes] [-z] [script]
Tools designed to aid understanding of complex data sets and
relationships between them

e.g. through visualisation
Make it all easy to use!




Scientists should not have to be Linux script experts,
…nor set up/configure complex Grid software or follow complex procedures for getting,
using Grid certificates,
…nor have detailed understanding of low level data schemas for all data sites,
… etc etc
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Overview of BRIDGES
Biomedical Research Informatics Delivered by Grid
Enabled Services (BRIDGES)
NeSC (Edinburgh and Glasgow) and IBM
Started October 2003 – due to end soon
Supporting project for CFG project
Generating data on hypertension
Rat, Mouse, Human genome databases
Variety of tools used
BLAST, BLAT, Gene Prediction, visualisation, …
Variety of data sources and formats
Microarray data, genome DBs, project partner research data, …
Aim is integrated infrastructure supporting
Data federation
Security
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BRIDGES Project
CFG Virtual
Publically Curated Data
Ensembl
Organisation
OMIM
Glasgow
SWISS-PROT
Private
Edinburgh
MGI
VO Authorisation
Private
data
Oxford
Information
Integrator
Synteny
Service
Magna
Vista
Service
London
HUGO
…
RGD
Leicester
DATA
HUB
OGSA-DAI
Private
data
data
Private
data
Netherlands
Private
data
Private
data
+
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+
+
Primary BRIDGES Data Use Case
Given gene name/identifier, issue a query to federated database and present all
available information back to the user in a user friendly/configurable way
Several client side applications were developed for this purpose:
MagnaVista, GeneVista, “JOS-AHM-vista”


MagnaVista and “JOSAHM-vista” are Java applications
GeneVista based upon portlet technologies
Notes


focus was on developing working solutions for scientists and not to compare OGSA-DAI and IBM II
several team changes throughout project
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Overview of Data Access and
Integration Technologies
Overview of Information Integrator
suite of wrappers for relational (Oracle, DB2, Sybase, …) and nonrelational (flat files, Excel spreadsheets, XML databases, …) targets
which extend integration capabilities of DB2 database

allows to establish ‘federated’ view of distributed data allowing applications
access to data as though in single, local DB2 database
Data




Data in
DB2
wrapper
Client Running
Life Sciences App
wrapper
Information
Integrator
wrapper
SQL API
(JDBC,
ODBC, )
Data in
Oracle DB
Data in
Flat files
Catalogue
free for academic use (IBM Scholars program)
comes with suite of tools and utilities with which DB administrator can monitor
and optimize database
can interact with DB either by command line or graphical interface
options to create Java/SQL stored procedures and customized functions
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Overview of Data Access and
Integration Technologies
Overview of OGSA-DAI middleware
provides application developers with a range of service interfaces
allowing data access and integration via the Grid
OGSA-DAI is not a database management system


rather it uses Grid infrastructure to perform queries on a set of relational/nonrelational data sources and conveys result sets back to the user application via
SOAP
Through OGSA-DAI interfaces, disparate, heterogeneous data sources and
resources can be treated as a single logical resource
OGSA-DAI is


free/open source
has number of data source types both relational and non-relational with which it
can communicate
OGSA-DAI documentation is clear/concise
(We’ve had!) good support from the development team
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Comparing Data Access and Integration
Technologies
How to compare?
Set-up installation
Post-installation
Initial user experiences
Challenges of life sciences


Schema Changes
Data Independence
Creating Federated Views
Performance
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Set-Up Installation
IBM Information Integrator
Process of accessing, obtaining, installing and configuring IBM II is non-trivial

Access through “Scholar’s Program” can be a time consuming procedure and requires authorisation
Advanced knowledge of the vendor clients that the wrappers may use (e.g. Sybase
12.5ASE Client) eases the installation process

especially true on Linux as need to manually edit config. files/run rebinding scripts if clients installed later
BRIDGES team also went on training course from IBM which helped
OGSA-DAI
is (by contrast) a much friendlier affair
one visits the download site, signs up for access and is issued with a username and
password for authentication to the download area
new releases are advertised by email (submitted during the sign up process)
all downloads supplied with obligatory README file which provides

guidance as to the setup procedure and additional downloads needed
– e.g. JDBC drivers, apache utilities
With OGSA-DAIv4 release the install process can also be done via a GUI
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Post-Installation
IBM Information Integrator
IBM provides MANY!!!!! Redbooks available on their website
at the time of BRIDGES work in applying IBM II, these were not
descriptively named so it was a matter of opening each one to discover
title/topics dealt with

time consuming searching for specific information
Online search facility useful especially for syntax questions
Within the last few months, navigation around IBM’s website has improved
significantly providing easier access to online documentation and resources
OGSA-DAI
comes with its own HTML documentation which can be downloaded
separately as required
content and navigability of this has improved over each release as more
detailed coding examples have been given
User support is quick and efficient with a response time typically < 24 hours
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Basic Usage Experience
IBM Information Integrator
Attempts were made initially to use IBM II’s XML wrapper to query
Swissprot/Uniprot DB


DB is in XML format and available for ftp download (over 1.1GB)
wrapper failed in its attempt to work with this file, as, according to IBM white paper the whole
document is loaded in memory as a Document Object Model (DOM)
– Could have split the file into chunks but cumbersome solution
Decided to parse the file and import it into DB2 relational tables

Each flat file wrapper has to be manually configured to match the file ‘columns’
– no greater effort to actually write a programme to parse the file and then add to DB
» Once in DB have all the benefits of indexing, optimisation etc

initial parse of the Swissprot DB used table ‘Inserts’ to commit data immediately to DB2
database as file read by the parsing program
– Java SAX parsing used and primary and foreign keys updated using insert triggers
– took 84 hours for the 1.1GB file with around 500,000 inserts to the database
Wrapper format inconsistencies, e.g. OMIM
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Basic Usage Experience …ctd
IBM Information Integrator
IBM II insists that the flat file being wrapped exists on a
computer with exactly the same user setup/privileges as
the data server itself


not the case with the BRIDGES federated data Grid!!
– unlikely to be the case with other life science data sets…???
Fine grained security model something explored within BRIDGES
based upon PERMIS technology
– (see demo at NeSC booth for more info)
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Basic Usage Experience …ctd
OGSA-DAI
Used basic Perform documents for doing federated
queries
Returned data stored locally (in files) and accessed by
client application and rendered to users
Is this Integration?

From client perspective, they see no difference!!!
– More elegant solution would be to have middleware do “integration”
but issues…
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Schema Changes
In BRIDGES two-relational data sources allowed programmatic access:
Ensembl (MySQL - Rat, Mouse and Human Genomes, Homologs and
Database Cross Referencing)
MGI (Sybase - mainly Mouse publications and some QTL data.)
Flat files downloaded for
RGD (Rat Genome Database), OMIM (Online Mendelian Inheritance in Man),
Swissprot/Uniprot, HUGO (Human Gene Ontology), GO (Gene Ontology)

Don’t expect to be give schema for flat file!!!
Changes made to schema of third party DB completely out of our control
Ensembl change the name of their main gene database every month!

DB schema drastically altered on 3 occasions during BRIDGES project
MGI have had one major overhaul of all their table structure
In these cases queries to these remote data sources will fail!!!
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Schema Changes …ctd
We used Materialized Query Tables (MQTs) in IBM II to insulate
queries from remote schema changes
MQT is local cache of remote table/view and can be set to refresh after a
specified time interval or not at all

up to the minute data (refreshed frequently) vs slightly older data but impervious to schema
changes
MQT can be optimized to try the remote connection first, if available run
query, if not use local cache

Query fails if remote schema changes!!!
Bridges_wget application
checks for remote DB connections

if the connection made – runs sample query naming columns to see if schema has changed
– If all is well, remote flat files are checked for modification dates
– If newer ones found they are downloaded, parsed and loaded into the DB
» Goes some way to keeping the BRIDGES DB up to date with current data
– Parsers are not semantically intelligent so require updating the code (Java) to meet
with file format modifications
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Data Independence
Key issue challenge is fact that data sources largely independent
Not always possible to find column to act as foreign key over which joining
two (or more) databases can occur
When there is a candidate, often the column name is not named
descriptively to give clue as to which database might be joined to which
For example, in case of Ensembl a row containing a gene identifier contains
a Boolean column indicating whether a reference exists in another database

RGD_BOOL=1 indicates that a cross reference can be made to the RGD database for this
gene identifier
– Must query Ensembl RGD_XREF table to obtain unique ID for entry in RGD database
– Query to RGD may contain references to other databases and indeed back to Ensembl
» …potentially have circular referencing problem!!!

Solved by caching all available unique identifiers and their associated database from all
remote data sources in local materialized query table
– When match found, associated data resource queried and all results returned to user
» Up to user to decide which information to use
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Creating Federated Views
In setting up federated view with IBM II various
steps needed:
choose which wrapper to use;
define a ‘Server’ containing all connection parameters;
create ‘Nicknames’ for the server

local DB2 tables mapped to their remote counterparts;
‘Discover’ function supports this process
connects to the remote resource and displays all the
metadata available
Such advanced features are not available with
OGSA-DAI
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Performance Comparison
Example of single query response, we ran a search for the
PAX7 gene across the BRIDGES federated view of 7 bio
databases. This returned
One entry from Ensembl Mouse Table (27 columns)
One entry from Ensembl Human Table (27 columns)
One entry from the HUGO database (20 columns)
Eighty five entries from MGI including full abstract and publication
details. (11 columns)
One full entry from the OMIM database including fully annotated
publication details. (19 columns)
Two full entries from Swissprot/Uniprot including full sequence and
reference data. (50 columns)
The average response time for MagnaVista was 44 sec
includes time to rebuild the application perspective GUI
OGSA-DAI solutions are of this order also
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Conclusions
Big advantage of using IBM II is all utilities that come with database
management system. This includes :
replication of databases which can be configured to update from single
transaction committed to a set time interval for bulk updates;
creation of explain tables which will graphically show the query author the
amount of table scans done as the result of the executed query and thereby
allow different solutions to be compared;
creation of tasks which can be executed immediately or at specified times,
e.g. when the database is less used;
running statistics and reorganizing tables;
taking Snapshots of the database to see where bottlenecks may be
occurring.
OGSA-DAI as used in BRIDGES has shown we can implement data
“access” solutions also
Less overheads in learning DB2
We note that since our evaluations were made, IBM have prototyped an
OGSA-DAI wrapper for Information Integrator.
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Conclusions
We focused largely on data access (and not integration)
Client apps took care of majority of the data integration issues
Tried to explore OGSA-DQP but without immediate success
Changes in personnel, keeping IBM II solution alive!
Future challenges and recommendations
standards/data models crucial to data access and integration
often gaining access to the database itself most often not possible

JDSS report describes these issues in detail
BRIDGES queries fairly simplistic in nature – returning all
data sets associated with a named gene
GEMEPS project looking towards more complex queries,


e.g. lists of genes that have been expressed and their up/down expression
values as might arise in microarray experiments
Collaboration with Cornell and Riken Institute, Japan
BRIDGES to be refined/extended and used within the (not
so!) recently funded Scottish Bioinformatics Research
Network
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DEMO
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