Usage Principles

Workflow

To import and begin using danRerLib in your Python project, import danrerlib as:

import danrerlib as drl

To import a specific module, for example the mapping module, you can import as:

from danrerlib import mapping

A typical workflow of danRerLib depends on the feature you wish to use (see Introduction). Please the tutorial page for some typical workflows.

Zebrafish Gene ID Types

This toolkit supports conversions between a variety of gene id types for the zebrafish. To properly identify the correct gene id, please utilize the following reference. The currently supported zebrafish Gene ID types are:

Gene ID Type	Referenced as	Description	More Information
NCBI Gene ID	`'NCBI Gene ID' (str)` or `danrerlib.NCBI_ID`	Integer Gene IF managed by NCBI, also known as Entrez Gene ID	NCBI Website
ZFIN ID	`'ZFIN ID' (str)` or `danrerlib.ZFIN_ID`	Each ID starts with ‘ZFIN-’ and is managed by the Zebrafish Information Network	ZFIN Website
Ensembl ID	`'Ensembl ID' (str)` or `danrerlib.ENS_ID`	Each ID starts with ‘ENSDAR’ and is managed by ensembl	Ensembl Website
Symbol	`'Symbol' (str)` or `danrerlib.SYMBOL`	A alphabetic gene id type where the nomenclature is managed by ZFIN and further supported by NCBI	ZFIN Website

Mor information can also be found in Tutorials and the API Reference.

Human Gene ID Type

This toolkit supports orthology mapping between zebrafish and human. It is possible to map to and from any of the above zebrafish gene id types listed and the supported human gene id type. The currently supported human gene ID type:

Gene ID Type	Referenced as	Description	More Information
NCBI Gene ID	‘NCBI Gene ID’ (str) or NCBI_ID	Integer Gene ID managed by NCBI, also known as Entrez Gene ID	NCBI Website

More information can also be found in Tutorials and the API Reference for the mapping module.

Pathway Databases

This toolkit supports the retrieval and usage of a variety of pathway and annotation databases. Each database has been developed using unique modules within the toolkit. The currently supported databases are:

Database	Module	Short Description	More Information
KEGG Pathway	`danrerlib.KEGG`	Collection of curated biochemical pathways	KEGG Pathway Website
KEGG Disease	`danrerlib.KEGG`	Database cataloging human diseases and genes	KEGG Disease Website
Gene Ontology Biological Processes (GO BP)	`danrerlib.GO`	Standardized vocabulary for biological processes	Gene Ontology Website
Gene Ontology Molecular Function (GO MF)	`danrerlib.GO`	Description of molecular activities of genes	Gene Ontology Website
Gene Ontology Cellular Components (GO CC)	`danrerlib.GO`	Definition of subcellular structures and locations	Gene Ontology Website

Enrichment Types

This toolkit supports enrichment testing for the pathway and annotation databases listed above. Enrichment testing, also known as pathway enrichment analysis or functional enrichment analysis, is used to determine if a set of genes associated with a specific biological process, function, or pathway is statistically overrepresented or underrepresented compared to what would be expected by chance. A variety of statistical tests can be used to determine over or under representation. The currently supported methods are:

Enrichment Analysis Method	Function	Short Description	More Information
Fisher’s Exact Test	`danrerlib.enrichment.enrich_fishers()`	Statistical test for pathway enrichment	More Info
Logistic Regression	`danrerlib.enrichment.enrich_logistic()`	Regression-based method for enrichment	More Info