Category Genomics>Genetic Data Analysis/Tools and Cross-Omics>Data/Text Mining Systems/Tools

Abstract Phenote is both a complete piece of software and a software toolkit designed to facilitate the annotation of biological phenotypes using ontologies.

It provides an interface and infrastructure to record genotype-phenotype pairs, together with the provenance for the annotation.

Typical users of Phenote include literature curators, laboratory researchers, and clinicians looking for a method to record data in a user-friendly and computable way.

Phenote was originally developed as a literature curation tool for biomedical phenotype annotation; Phenote can be customized to suit any user-specified domain requiring ontology-based annotation.

The all-in-one interface allows users to quickly search and view corresponding metadata for relevant ontology terms during data entry, browse through the ontology in a navigation window, and view the data annotations in a spreadsheet-like window.

Data recorded with Phenote can be ported to any external application or database using one of its built-in data adapters, or by plugging in a custom data adapter generated with the application programming interface (API).

Advantages to Annotation with Ontologies --

The power of Phenote lies in its use of ontologies to represent concepts during data annotation.

Ontologies represent domains of knowledge by defining concepts or entities within a particular domain (and the terms used to refer to them) as well as how the entities are related to each other.

Ontology thus provides a vocabulary for communicating knowledge about a topic, as well as a computable representation of the underlying reality.

Use of a well-structured, domain-expert-reviewed ontology for biological annotation allows the curated data to be understandable by both humans and computers, thereby increasing the capacity for meaningful analysis.

Using ontologies during annotation has several advantages:

1) User errors (such as typos) during data entry are minimized, delivering better-quality datasets;

2) Because each term in an ontology is defined, exact concepts are used and recorded, rather than “fuzzy” human language, in which words and phrases can have more than one meaning; and

3) Given the proper analysis tools, the resulting data can be mined in the context of the broader knowledge contained within the ontologies.

Annotations recorded by Phenote are based on the ‘EQ Model’ for representing phenotypes, combining entities from any ontology with qualities (such as PATO) (see below...).

Phenote Features include:

1) Spreadsheet-like interface;

2) Type-ahead-suggest (autocompletion) for terms within ontologies;

3) Use of any OBO-format ontology [or Web Ontology Language (OWL) (to) Open Biomedical Ontologies (OBO) converted];

4) Ontology navigation and all-in-one term information display;

5) Bulk copy/edit/delete/sort of phenotype-genotype character entries;

6) Excel-compatible output format (tab-delimited);

7) Additional export formats: pheno-syntax, XML;

Pheno-syntax - Pheno-syntax is a compact syntax for the representation of phenotypes using ontologies.

8) Configurable input fields to allow custom data entry and user-specified ontologies;

9) Pre-configurations available for different communities; and

10) Easy to customize your own configuration.

PATO quality ontology and post-composed phenotype descriptions --

Some model organisms, such as zebrafish and Drosophila, do Not use species-centric phenotype ontologies but rather have opted for a compositional approach.

That is, instead of choosing from predetermined lists of phenotypes, curators have the ability to compose descriptions of phenotypes on-the-fly using a combination of classes from several ontologies, including the ontology of qualities termed Phenotype and Trait Ontology (PATO).

These composed descriptions minimally consist of at least two (2) variables:

1) The entity that is observed to be affected (for example, head, liver, Purkinje cell, and so on); and

2) The specific characteristic or quality of that entity affected (for example, size, color, shape, structure…).

This is dubbed the ‘EQ’ model.

The E variable is filled with a class from any OBO ontology [for example, Foundational Model of Anatomy (FMA), Mouse adult gross anatomy (MA), Mouse gross anatomy and development (EMAP) or Cell type (CL)] and the Q variable is filled with a class from PATO.

PATO covers both general qualities (for example, shape) and specific qualities (for example, branched), connected in a hierarchy of is a relations.

This EQ approach has been used in the annotation of human genotype-phenotype associations, as well as in model organism databases such as FlyBase (Drosophila) and ZFIN (zebrafish).

The manufacturers conclude that EQ-based annotation of phenotypes, in conjunction with cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone.

This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.

System Requirements

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Manufacturer Web Site Phenote

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G6G Abstract Number 20644

G6G Manufacturer Number 104242