Dataset Information

CART-a chemical annotation retrieval toolkit.

ABSTRACT:

Motivation

Data on bioactivities of drug-like chemicals are rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases.

Results

To address this challenge, we have developed CART, a Chemical Annotation Retrieval Toolkit. As a key functionality, it matches an input list of chemical names into a comprehensive reference space to assign unambiguous chemical identifiers. In this unified space, bioactivity annotations can be easily retrieved from databases covering a wide variety of chemical effects on biological systems. Subsequently, CART can determine annotations enriched in the input set of chemicals and display these in tabular format and interactive network visualizations, thereby facilitating integrative analysis of chemical bioactivity data.

Availability and implementation

CART is available as a Galaxy web service (cart.embl.de). Source code and an easy-to-install command line tool can also be obtained from the web site.

Contact

bork@embl.de

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Deghou S

PROVIDER: S-EPMC5018367 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CART-a chemical annotation retrieval toolkit.

Deghou Samy S Zeller Georg G Iskar Murat M Driessen Marja M Castillo Mercedes M van Noort Vera V Bork Peer P

Bioinformatics (Oxford, England) 20160602 18

<h4>Motivation</h4>Data on bioactivities of drug-like chemicals are rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases.<h4>Results</h4>To address this challenge, we have developed CART, a Chemical Annotation Retrieval Toolkit. As a key functionality ...[more]

PMID: 27256313

Similar Datasets

Project description:BackgroundThe Gene Ontology Annotation (GOA) database http://www.ebi.ac.uk/GOA aims to provide high-quality supplementary GO annotation to proteins in the UniProt Knowledgebase. Like many other biological databases, GOA gathers much of its content from the careful manual curation of literature. However, as both the volume of literature and of proteins requiring characterization increases, the manual processing capability can become overloaded. Consequently, semi-automated aids are often employed to expedite the curation process. Traditionally, electronic techniques in GOA depend largely on exploiting the knowledge in existing resources such as InterPro. However, in recent years, text mining has been hailed as a potentially useful tool to aid the curation process. To encourage the development of such tools, the GOA team at EBI agreed to take part in the functional annotation task of the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) challenge. BioCreAtIvE task 2 was an experiment to test if automatically derived classification using information retrieval and extraction could assist expert biologists in the annotation of the GO vocabulary to the proteins in the UniProt Knowledgebase. GOA provided the training corpus of over 9000 manual GO annotations extracted from the literature. For the test set, we provided a corpus of 200 new Journal of Biological Chemistry articles used to annotate 286 human proteins with GO terms. A team of experts manually evaluated the results of 9 participating groups, each of which provided highlighted sentences to support their GO and protein annotation predictions. Here, we give a biological perspective on the evaluation, explain how we annotate GO using literature and offer some suggestions to improve the precision of future text-retrieval and extraction techniques. Finally, we provide the results of the first inter-annotator agreement study for manual GO curation, as well as an assessment of our current electronic GO annotation strategies.ResultsThe GOA database currently extracts GO annotation from the literature with 91 to 100% precision, and at least 72% recall. This creates a particularly high threshold for text mining systems which in BioCreAtIvE task 2 (GO annotation extraction and retrieval) initial results precisely predicted GO terms only 10 to 20% of the time.ConclusionImprovements in the performance and accuracy of text mining for GO terms should be expected in the next BioCreAtIvE challenge. In the meantime the manual and electronic GO annotation strategies already employed by GOA will provide high quality annotations.

Dataset Information

CART-a chemical annotation retrieval toolkit.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

CART-a chemical annotation retrieval toolkit.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets