Opportunities for text mining in the FlyBase genetic literature curation workflow by Peter McQuilton. (Database (2012) 2012 : bas039 doi: 10.1093/database/bas039)
Abstract:
FlyBase is the model organism database for Drosophila genetic and genomic information. Over the last 20 years, FlyBase has had to adapt and change to keep abreast of advances in biology and database design. We are continually looking for ways to improve curation efficiency and efficacy. Genetic literature curation focuses on the extraction of genetic entities (e.g. genes, mutant alleles, transgenic constructs) and their associated phenotypes and Gene Ontology terms from the published literature. Over 2000 Drosophila research articles are now published every year. These articles are becoming ever more data-rich and there is a growing need for text mining to shoulder some of the burden of paper triage and data extraction. In this article, we describe our curation workflow, along with some of the problems and bottlenecks therein, and highlight the opportunities for text mining. We do so in the hope of encouraging the BioCreative community to help us to develop effective methods to mine this torrent of information.
Database URL: http://flybase.org
Would you believe that ambiguity is problem #1 and describing relationships is another one?
The most common problem encountered during curation is an ambiguous genetic entity (gene, mutant allele, transgene, etc.). This situation can arise when no unique identifier (such as a FlyBase gene identifier (FBgn) or a computed gene (CG) number for genes), or an accurate and explicit reference for a mutant or transgenic line is given. Ambiguity is a particular problem when a generic symbol/ name is used (e.g. ‘Actin’ or UAS-Notch), or when a symbol/ name is used that is a synonym for a different entity (e.g. ‘ras’ is the current FlyBase symbol for the ‘raspberry’ gene, FBgn0003204, but is often used in the literature to refer to the ‘Ras85D’ gene, FBgn0003205). A further issue is that some symbols only differ in case-sensitivity for the first character, for example, the genes symbols ‘dl’ (dorsal) and ‘Dl’ (Delta). These ambiguities can usually be resolved by searching for associated details about the entity in the article (e.g. the use of a specific mutant allele can identify the gene being discussed) or by consulting the supplemental information for additional details. Sometimes we have to do some analysis ourselves, such as performing a BLAST search using any sequence data present in the article or supplementary files or executing an in-house script to report those entities used by a specified author in previously curated articles. As a final step, if we cannot resolve a problem, we email the corresponding author for clarification. If the ambiguity still cannot be resolved, then a curator will either associate a generic/unspecified entry for that entity with the article, or else omit the entity and add a (non-public) note to the curation record explaining the situation, with the hope that future publications will resolve the issue.
One of the more esoteric problems found in curation is the fact that multiple relationships exist between the curated data types. For example, the ‘dppEP2232 allele’ is caused by the ‘P{EP}dppEP2232 insertion’ and disrupts the ‘dpp gene’. This can cause problems for text-mining assisted curation, as the data can be attributed to the wrong object due to sentence structure or the requirement of back- ground or contextual knowledge found in other parts of the article. In cases like this, detailed knowledge of the FlyBase proforma and curation rules, as well as a good knowledge of Drosophila biology, is necessary to ensure the correct proforma field is filled in. This is one of the reasons why we believe text-mining methods will assist manual curation rather than replace it in the near term.
I like the “manual curation” line. Curation is a task best performed by a sentient being.