Extracción de miel

3.3. Data structure: the LPD schema

The different types of data in the LPD were used to derive a logical conceptual data model, which was implemented in a relational setting using the MySQL platform. Thus, major entities in the data were identified and captured in tabular structures that include a specification of the entity properties and attributes. Relationships between the entities were also modelled that indicate how instances from different entities relate to each other. The diagram on Figure 3.3.1 shows the LPD data structure. Importantly, tables from each data type (consisting of expression data, annotation and domain data) are shown in different colours. The LPD data model including entities and their relationships is discussed in full in the following paragraphs.

Microarray pain Study one one one one one one many many - feature_id: char - seq_id: char(32) - genBank_id: char one

Figure 3.3.1. The LPD data structure. Green tables correspond to gene expression data including experiment annotations, blue tables store one

3. Adatabase of gene expression data from animal models of peripheral neuropathy

3.3. Data structure: the LPD schema

3.3.1. Domain data tables

Beginning with the biological domain data, the LPD schema features one unique entity: the Pain Model or perhaps more appropriately the Experimental model entity. Owing to variations in the experimental procedures used to realise these animal models, only basic but common features of the models were taken to define the attributes of the representative class Pain Model. These consisted of the model common names, the original study that first developed the model and keywords capturing the pathological and phenotypic characteristics of the model. The latter may be more formally expressed using the Mammalian phenotype ontology (Smith et al., 2005), part of the Open Biomedical Ontologies (OBO) .

3.3.2. Gene expression data tables

As for the gene expression data, two main entity classes were recognised: the Microarray Pain Study class and the Gene List class. The former class captures summaries of microarray experiments, including information on the experimenter and various useful experimental details such as the animal model investigated (hence the link to the Pain Model entity), species/strain information, array platform and handling of the RNA material. The Gene List

3. Adatabase of gene expression data from animal models of peripheral neuropathy

3.3. Data structure: the LPD schema

class on the other hand, captures the gene expression data outcome of the microarray study; in particular, genes found most differentially expressed and their fold changes. Importantly, with some array platforms such as Affymetrix, the expression measurement is identified with a probeset identifier instead of the gene identifier and many probesets may map to the same gene. Consequently, the Gene List entity features a generic feature_identifier attribute, which can take the value of an Affymetrix probeset identifier or a gene identifier (usually GenBank or UniGene).

3.3.3. Functional annotation data tables

A number of tables exist in the LPD that hold functional annotations of the array genes, corresponding to different sources of annotation. These include the Affymetrix Annotation table, the Ensembl Annotation table and the GO/KEGG Annotation tables derived from BioMap. Logically, functional annotations should be modelled as a single entity since the source of annotation is merely an attribute of the annotation. However, owing to the differences in the way functional information is encoded in each source database and also for ease of maintenance, it was decided to keep annotations from the different sources in separate tables. For instance, with Ensembl and unlike the rest of the source databases, the GO annotation terms from all three

3. Adatabase of gene expression data from animal models of peripheral neuropathy

3.3. Data structure: the LPD schema

ontologies: functional process, molecular function and cellular component are given together in a single string without indication of their ontology type. It is important to note that the reason why annotations from different sources were pulled together in the LPD is because it was noticed that they complemented each other and for many array genes, functional annotations were only available from one source and not the rest.

The BioMap functional annotation data have the special feature of being linked to protein family classification data and are arranged in a special data table structure that requires more explanation. One key table is Cluster Data. This table was mirrored from the BioMap database and hosts information on family classification of sequences by linking all BioMap proteins to their corresponding sequence cluster numbers. Each protein entry in Cluster Data is functionally annotated via association, where possible, with one or more entries from the GO Annotation and KEGG Annotation tables.

As an interface between the gene expression data and the functional annotation data, an additional table capturing the entity Gene was created. Importantly, the latter defines important information for each array feature, notably sequence and identifier attributes of the corresponding gene. This has the important consequence of revealing probeset association to identical genes

3. Adatabase of gene expression data from animal models of peripheral neuropathy

3.3. Data structure: the LPD schema

with Affymetrix based expression datasets (more details will follow in the next section).

Importantly, the association to BioMap protein identifiers in the Gene table allows each array feature (gene) to be linked to the corresponding BioMap cluster number, by reference to the Cluster Data table. Knowing the BioMap cluster number for a given array feature (gene) allows functional information to be retrieved from homologous BioMap proteins at a desired level of sequence identity. For instance, if an array feature/gene associated BioMap cluster number is 1.2.1.3.4.1.5.3.6.1.1, then all BioMap proteins with BioMap cluster numbers beginning with the same first four digits 1.2.1.3 in Cluster Data, are in the same S40 cluster; that is sharing at least 40% sequence with the array gene protein. Functional annotations may then be inherited from these homologs from the GO and KEGG annotation tables (for more details on BioMap cluster numbers, refer to Figure 3.1.2).

3. Adatabase of gene expression data from animal models of peripheral neuropathy