Chado Manual

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The Chado schema has been designed with modularity and compartmentalization of function in mind. Groups of tables concerned with a single knowledge domain are called modules. There is a core module, general, concerned with data underlying all other classes, these tables store information about databases, databases identifiers, and general information about Chado tables. Equal in importance in Chado is cv, the module concerned with controlled vocabularies or ontologies.

All other sets of tables, or modules, link to these general and cv tables directly or indirectly but are limited in scope to specific biological domains. For example, the sequence module is concerned with protein and nucleotide sequence, the pub module is concerned with articles and publications, and so on. In addition to these limitations in scope we see an effective absence of redundancy. For example, there is a module called companalysis, short for ’’computational analysis”. Its tables are responsible for describing algorithms and the output of algorithms. The rad module (for microarrays) uses companalysis in order to refer to algorithms in addition. The uniqueness, and generality, of the modules implies that one can rely on pre-existing modules for function if one is interested in introducing new modules.

Chado should be considered a highly extensible database due to its modular design. The clear segregation of function into modules, or sets of tables, should allow the introduction of new modules covering new domains of knowledge. The fact that new modules have been introduced into Chado since its initial release (e.g. phylogeny, rad, stock) confirms that the authors' design concepts were correct.


One of the more profound, recent changes in the nature of biology has to do with the adoption of ontologies, or controlled vocabularies, as a way to describe and organize data. Our most popular ontologies have arisen from the need to describe the remarkable variety of living things, and are very detailed and broad. Simultaneously these ontologies have served to categorize and classify the contents of entire databases that had been previously been atomized, or only partially coherent. Chado has been built from the outset to integrate with these ontologies, and this feature makes it extremely expressive. The acceptance of ontologies as general standards, and Chado’s use of these ontologies, make Chado an excellent platform for annotation of biological data.

Associated Software

Chado is considered to be one of the key components in the GMOD suite. It should not be considered the database of GMOD, there are other ways of storing data within GMOD (e.g. BioSQL, Bio::DB::GFF, Bio::DB::SeqFeature) but it is the database of choice when complexity and breadth are required. Because of this central position in GMOD it has been the focus of much software development, not just the design of the schema itself but in terms of components using it and adaptor software that connect Chado to some other GMOD component. Thus we are able to use Chado with different browsers such as GBrowse and Apollo (the latter being able to both read from and write to the database). We have a variety of tools that can be used to load sequence data as GFF into Chado and there are different utilities that can move complex data in and out of Chado as XML (XORT, GMODTools).

Complexity and Detail

Part of the impetus for the creation of Chado was the need for a database that could describe all the detail that arises from extensive research done on model organisms. There are a number of schemas available that are built upon on the classic, and simple, concept of the central dogma, from 1 gene to 1 RNA to protein. However it can become difficult to work with simpler schemas when one wants to describe trans-splicing or non-coding genes or other departures from the simple model that are commonly found in viral or bacterial genomes. The Chado schema is in use at model organism databases such as FlyBase and XenBase and the close ties between the Chado developers and these model organism communities has resulted in a extensively tested data model, capable of handling both detail and departures from the standard model.

Data Integration

One characteristic of biology over the past decades has been the major impact of methods or technologies. With each new adopted approach biologists have created sizable data repositories, either as private collections or in the public domain. The value and meaning of these data types is fully realized when they are tied to other types of data. For example, an evolutionary biologist may be studying the evolution of non-coding, regulatory sequences. In order to do this she will consider integrating expression data from microarrays (using the rad module) with sequence data (stored using the sequence module) from various species (the organism module) with phylogenetic trees (phylogeny module) with results from sequence comparison studies (companalysis module). This is not to suggest that such an effort is easy, rather that with a schema like Chado such a proposition is actually possible.


The community using Chado, and GMOD, is extensive and growing. Chado is in use at a number of sites worldwide (see GMOD Users) and is being considered by researchers in related fields, notably molecular evolution and ecology. The Chado community is active and supports a number of different mailing lists. It is also notable that there are close ties not just between GMOD and Chado users but also between these two groups and the community of ontology developers (for example, OBO, NCBO).


We organised the tables into distinct modular components with tightly defined dependencies. This is recogised as good software engineering practice, it allows different software components to focus on the specific data compartments required. It allows for extensibility and schema evolution within specific modules without disrupting the rest of the schema. Finally, it allows for a mix and match approach - it is the authors' hope that the schema modules will be adopted by other model organism and bioinformatics groups; these groups may want to swap in their own table variants within specific modules, or add modules of their own.

Module Dependencies

There is one module, general, that does not depend on or inherit from any other module. All other modules require general or some other module. Many modules require the sequence module or the cv module, or both.

Module Depends on
general none
organism general cv
pub general cv
cv general
sequence cv general pub organism
genetic sequence cv general pub phenotype
expression sequence cv pub
map sequence cv pub
rad sequence cv pub organism contact general companalysis
companalysis sequence cv
contact cv
library sequence cv pub organism
phenotype cv sequence
phylogeny sequence cv pub organism general
stock cv pub general organism genetic
www sequence cv pub phenotype organism expression general genetic

Inter-module Linking Tables

These can be thought of as floating outside of the respective modules they bridge, although they are generally bundled with one or the other module.

Linking Table Module Module
biomaterial_dbxref rad general
cvterm_dbxref cv general
environment_cvterm phenotype cv
expression_cvterm cv expression
expression_pub pub expression
feature_cvterm cv sequence
feature_cvterm_dbxref sequence general
feature_cvterm_pub sequence pub
feature_dbxref general sequence
feature_expression sequence expression
feature_genotype sequence genetic
feature_organism organism sequence
feature_phenotype sequence phenotype
feature_pub sequence pub
feature_relationship_pub sequence pub
feature_relationshipprop_pub sequence pub
feature_synonym general sequence
featureloc_pub sequence pub
featuremap_pub sequence pub
featureprop_pub pub sequence
gene_synonym general sequence
journal_dbxref general pub
library_cvterm library cv
library_feature library sequence
library_pub library pub
organism_dbxref general organism
phenotype_cvterm cv genetic
phylonode_dbxref phylogeny general
phylonode_organism phylogeny organism
phylonode_pub phylogeny pub
phylotree_pub phylogeny pub
pub_dbxref general pub
stock_cvterm stock cv
stock_dbxref stock general
stock_genotype stock genetic
stock_pub stock pub
stock_relationship_pub stock pub
stockprop_pub stock pub
wwwuser_author www pub
wwwuser_cvterm www cv
wwwuser_expression www expression
wwwuser_feature www sequence
wwwuser_genotype www genetic
wwwuser_organism www organism
wwwuser_phenotype www phenotype
wwwuser_project www general
wwwuser_pub www pub

Chado Naming Conventions

Case sensitivity

We use lowercase in all tables and column names - DBMSs differ in how they treat case sensitivity. For example, Oracle will automatically capitalize everything. So it's best to be neutral and use lowercase.

Table names

In table names, we use underscores for linking tables; e.g. feature_dbxref is a linking table between feature and dbxref.

Where a table name is a noun phrase rather than a single noun, we concatenate the words together. For instance the table for describing feature properties is called featureprop. It could be argued this is harder to read, but it does allow consistent usage of underscores as above. FeatureProp could be used where it is known the DBMS is case insensitive.

Column names

In column names, we also use concatenated noun phrases, except in the case of primary or foreign keys, e.g. dbxref_id.

We try to keep column names unique where appropriate, which is useful for large join statements or views, in avoiding column name clash between different tables. The convention is to use an abbreviated form of the table name plus a noun describing the column, for instance fmin in the feature table. By consistently using abbreviated forms we stop column names getting too big (many DBMSs will complain about long column names).

Primary and foreign key names

We use the same column name for primary and foreign key columns - very useful for NATURAL JOIN statements.


Constraint names are a concatentation of table name, underscore, the letter c, and a digit. For example: feature_phenotype_c1.


Index names are a concatentation of table name, underscore, the string idx, and a digit. For example: feature_phenotype_idx1.


The names of views are lowercase. Where a table name is a noun phrase rather than a single noun, we concatenate the words together using the underscore. For example the view used to query for nucleotide motifs is called nucleotide_motif and the view used to find exons from pseudogenes is called pseudogenic_exon.

Design Patterns

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Module System

Module Metadata

View Layers

Views can be thought of as virtual tables. They provide a powerful abstraction layer over the database. All views should be portable across all DBMSs

Views in chado are defined on a per module basis. View definitions are maintained in the chado/modules/MODULE-NAME/views directory.

Included in the view directory are report views. These can usually be found in a file called chado/modules/MODULE-NAME/views/MODULE-NAME-report.sql

Collections of view definitions are bundled into packages, each package is a .sql file.

Inter-schema Bridges

GODB Bridge
BioSQL Bridge

DBMS Functions

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DBMS Functions in Chado are entirely optional.

Functions in chado are defined on a per module basis. Function definitions are maintained in the chado/modules/MODULE-NAME/functions directory.

Collections of function definitions are bundled into packages. Each package comes with an interface descriptions and one or more implementations.

Function Interface Definitions

The interface descriptions are stored in a *.sqlapi file. The syntax used is a variant of SQL and is intended primarily as a consistent way of providing information for human, although it should be parseable by software.

Here is an example, taken from the top of the chado/modules/sequence/functions/subsequence.sqlapi package. This package provides basic subsequencing functions. It has dependencies on two other function packages, declared at the top of the file. The package declares multiple functions, only the first of which is show here, a function for extracting subsequences from the sequence of a feature.

<sql> IMPORT reverse_complement(TEXT) FROM 'sequtil'; IMPORT get_feature_relationship_type_id(TEXT) FROM 'sequence-cv-helper';

-- basic subsequencing functions --

DECLARE FUNCTION subsequence( srcfeature_id INT REFERENCES feature(feature_id), fmin INT, fmax INT, strandINT )


COMMENT ON FUNCTION subsequence(INT,INT,INT,INT) IS 'extracts a subsequence from a feature referenced by srcfeature_id, within the interbase boundaries determined by fmin and fmax, reverse complementing if strand = -1. The sequence can be DNA or AA. Strand must always by >0 for AA sequences'; </sql>

Function Implementations

The goal is to provide implementations for different dialects of procedural SQL. Currently only PostgreSQL dialect is supported. The psql implementations are stored in *.plpgsql files.