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Revision as of 17:35, 22 February 2008
BIRD System (BIRD): Biological Integration and Retrieval Data was designed by Hoan Nguyen at LBGI laboratory (POCH Team) of IGBMC[1] Strasbourg
Contents
What is BIRD System
Scientific Context
Since 2000, thanks to the availability of the human genome and the rapid progress of biotechnologies and information technologies, numerous large biomedical datasets have been generated. Thus, modern biomedical information corresponds to a high volume of heterogeneous data that doubles in size every year (Statistics NCBI) and that covers very different data types, including patient data (from phenotypic, environmental or behavioral origins), gene data (including genome environment, gene expression status, enzymatic activity, gene product modification…) and the processes, protocols or treatments used to generate the information. In this context, systemic approaches are now being developed to analyze and compare this huge amount of information, in order to identify genes and to predict their functions in the cascade of events and networks involved for example, in the emergence of a disease. This requires the development of dynamic and powerful systems to store, assemble, integrate and process very large datasets from different sources. Recently, the Decrypthon initiative (Decrypthon), resulting from a collaboration between AFM/CNRS/IBM, has been instigated, firstly to develop a computing grid that connects hundreds of processors installed in various data-processing centres of French universities and, secondly to provide a facilitated access to the data for the scientific biological community. In the framework of the Decrypthon initiative, several biomedical projects are in progress requiring on the one hand, a strong computational capacity and on the other hand, the deployment in the grid environment of a data integration system able to manage automatically large volumes of heterogeneous data and to quickly process complex queries and versioning management.
BIRD System Overview
BIRD System (Nguyen et al, CORIA 2008, Hermes Edition) was designed to manage large collections of biological data and to intensive computation and simulation. BIRD heritages somes main idea of Saada project[2]. A generic configurable data model has been designed and allows the simultaneous integration of genomics, transcriptomics and ontology datasets using a limited number of product mapping rules provided by the user (operator or system administrator). The integration rules allow the easy creation of the database according to semantic topics and real requirements. BIRD is driven with a high level query engine (BIRD-QL), based on SQL and a full text engine allowing the biologist to quickly extract knowledge without programming. Thanks to such an engine, the system is capable to generate the sub-bank of data in accordance with the real requirement.
The hosted data can be accessed by the community using various methods such as a Web interface, Http Service, an API Java or a BIRD-QL Engine Query.
BIRD System is developed with the Java technology. BIRD System uses IBM DB2 for data server; Websphere Federtion Server for virtual databases. The web application is hosted by a Tomcat Server or by a WebSphere Application Server.
BIRD System is not only a retrieval data but also a plate-forme of Knowledge Discovery in Biological Database or an inductive database. We use IBM Miner Intelligent (association rules, classification, ..) in order to develop the data mining model. User could uses BIRD-QL for mining pertinent information or analyzing the relational patterns by using descriptive patterns of BIRD-QL engine.
The first goal of Bird System is to implementation of the Décrypthon Data Center [3] [4] in the framework of Décrypthon Programme (AFM/CNRS/IBM ) [5]
Data Format & DATABASES List
Format: EMBL, GENBANK, XML, CSV, OBO/OWL, PDB, UMD, Relational Schema (XML Metadata).
Databases: GENBANK, EST, WGS, REFSEQ, PDB, UNIPROT, UCSC, INTERPRO, GO, TAXONOMY, MACSIM, EVI-GENORET (local user), STRING (local user), UMD Data (local user), ...
BIRDQL Biological Query Language
The heterogeneous data integrated in BIRD System are represented by several relational tables. The exploitation of these data by SQL queries is not obvious except for developers or computer scientist experts.
Building queries with SQL in this context is not easy with because that requires to use joins (terme technique) to select data in multiple tables. This complexity must be hidden by HTML forms but a lot of queries can not be setup with HTML forms.
We proposes own query language (BIRDQL), there is new standard biological query language allowing the biologist or clinician to create data retrieval protocols without exhaustive knowledge of the data sources and their architecture. BIRD System is driven with a high level query engine: BIRDQL, which makes it possible for biologists to express easily queries and to extract knowledge by classical constraints and scientific functions (StructuralDistance,SequencePattern,AssociationRule...).
BIRDQL in not a mathematically complete language but indeed an idiom adpated to the GUI, human readable enough to be modified by hand. see more BIRDQL
BIRD Data Access Protocoles
Several protocoles are available see more BIRD Data Access Protocol
BIRD business intelligence
Theories and Functionalities
KDD Steps
KDD Tecnhique & Algorithm
KDD Data Model & View
Association rule learning
a.What Is Association Rule Mining?
Describing association relationships among the attributes in the set of relevant data
Frequent pattern mining: find all frequent patterns in a database
Frequent patterns: patterns (set of items, sequence, etc.) that occur frequently in a database [AIS93]
Frequent pattern mining: finding regularities in data
+What products were often purchased together? Beer and diapers?!
+What are the subsequent purchases after buying a product( ex. car)?
+Can we automatically profile patient or gene ?
b.Basic
Rule Definition
Body ==> Consequent [ Support , Confidence ]
(IF <> THEN <>)
Body: represents the examined data.
Consequent: represents a discovered property for the examined data.
Support: represents the percentage of the records satisfying the body or the consequent.
Confidence: represents the percentage of the records satisfying both the body and the
consequent to those satisfying only the body
Itemset: a set of items
=>E.g., acm={a, c, m}
Support of itemsets
=>Sup(acm)=3
Given min_sup=3, acm is a frequent pattern
Frequent pattern mining: find all frequent patterns in a database
c.Apriori Algorithm
Ck: Candidate itemset of size k Lk : frequent itemset of size k
L1 = {frequent items};
for (k = 1; Lk !=Q; k++) do
Ck+1 = candidates generated from Lk;
for each transaction t in database do increment the count of all candidates in Ck+1 that are
contained in t
Lk+1 = candidates in Ck+1 with min_support
return UkLk; (Union)
Kohonen´s feature maps
A Kohonen’s self organizing feayture map (K-map) is uses analogy with such biological neural structures where the placement of neurons is orderly and reflects structure of external (sensed) stimuli (e.g. in auditory and visual pathways).
K-map learns, when continuous-valued input vectors are presented to it without specifying the desired output. The weights of connections can adjust to regularities in input. Large number of examples is needed.
K-map mimics well learning in biological neural structures. It is usable in speech recognizer
This is a flat (two-dimensional) structure with connections between neighbors and connections from each input node to all its output nodes.
It learns clusters of input vectors without any help from teacher. Preserves closeness (topolgy).
Learning in K-maps
1. Initialize weights to small random numbers and set initial radius of neighborhood of nodes.
2. Get an input x1, …, xn.
3. Compute distance dj to each output node:
dj = (xi - wij)2
4. Select output node s with minimal distance ds.
5. Update weights for the node s and all nodes in its neighborhood:
wij´= wij + h* (xi - wij), where h<1 is a gain that decreases in time.
Repeat steps 2 - 5.
DB2 Miner Intelligent (API)
Data flow of the mining procedure (FindDeviations ex.)
Finding deviations
Finding groups with similar characteristics (ClusterTable procedure)
You can find groups with similar characteristics by using the ClusterTable procedure. When to do it: The database might contain patient data including demographic data, for example: v Gender v Age v Profession v Family statusThe information might also include the income or the socio-demographic group of the customer
Finding relationships (FindRules procedure) You can find relationships in your data by using the FindRules procedure.
Predicting future behavior (PredictColumn procedure)
In the tables or views of your database (Transciptomic or clinical Data), there might be one column that you are particularly interested in. In the clinical data, you can find relations between symptoms and diseases. With this information, you can predict the potential diseases of new patients
Finding most important fields (FindMostImpFields procedure)
You can find most important fields by using the FindMostImpFields procedure.
Kownledge Discovery in Biological Database
Some questions ?
� Can we perform sequence analysis in order to detect sequence patterns that occur very often in the chromosome?
� If a mutation takes place in a chromosome, does there exist any relationships between nucleotides? If yes, does a mutation of the one nucleotid also influence the other ones and can we use one of the techniques described above to find such relationships?
� If we translate the activities of the nucleotides into a frequency, can we then detect similar sequences that occur over time? Can we then find indicators that are probably responsible for mutation?
� Sequence tagged site (STS) are a short (200 to 500 base pairs) DNA sequence that has a single occurrence in the human genome. Can we detect such STSs using KDD?
� Genetic disorders resulting from the combined action of alleles of more than one gene (for example, heart disease, diabetes, and some cancers). Although such disorders are inherited, they depend on the simultaneous presence of several alleles; therefore, the hereditary patterns are usually more complex than those of single gene disorders. Can we detect such polygenic disorders using KDD ?
� A problem in bioinformatics is the determination of the order of the nucleotides in a DNA molecule or the order of amino acids in a protein. This is referenced as sequencing. Can we detect such poly genic disorders using KDD?
BIRD System in Action
Décrypthon Data Center
Overview
BIRD System is core of Décrypthon Data Center.
Sharing of large scare biological data for applications (Macsim, MS2PH, Macgos, Ordali..) runing on Décrypthon Grid. Managing of generated data (result) on grid
Sharing of data and services for scientific community http://bird.u-strasbg.fr:9080/BirdSystem/HomePage.do
Macsim uses BIRDQL engine
MACSIMS:Multiple Alignment of Complete Sequences Information Management System (Thompson et al, 2006).MACSIMS provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist .
Macsim gets direct connection with Bird database
GPS uses BIRDQL engine
Gscope utilise BIRD
Gscope peut se mettre maintenant en connexion directe avec Bird
- proc BirdFromQueryText {Texte {OutFile ""} {BirdUrl ""}}
- proc BirdFromQueryFile {Fichier {OutFile ""} {BirdUrl ""}}
Bird sait intégrer les fiches infos d'un projet Gscope. On peut alors les interroger directement par http ou par Gscope ou, mieux, par des affiches avec la commande BirdGscopeSearch
BIRD Development
see more BIRD Development
Publications
To cite BIRD System, please use the following publication;
1. Nguyen H., Berthommier G., Friedrich A., Poidevin L. ,Ripp R. , Moulinier L. and Poch O. Introduction du nouveau centre de données biomédicales Décrypthon, CORIA 2008, Hermes Edition.
2. "Conception of the BIRD System" is preparing for .....
3. "BIRDQL-A new Biological Query Language " is preparing for....
Contact
Nguyen Ngoc Hoan,PhD IGBMC Strasbourg 1 rue Laurent Fries BP 10142 67404 Illkirch CEDEX / France Mail:nguyen@igbmc.fr Tel: 0033 388653302
--Nguyen 15:07, 16 February 2008 (CET)---
