BackgroundThe task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.ResultsIn this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers.ConclusionsLINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at http://linnaeus.sourceforge.net/.

BackgroundWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).ResultsWe received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively.ConclusionsBy using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

Nephronophthisis (NPHP) is an autosomal recessive cystic kidney disease, caused by mutations of at least nine different genes. Several extrarenal manifestations characterize this disorder, including cerebellar defects, situs inversus and retinitis pigmentosa. While the clinical manifestations vary significantly in NPHP, mutations of NPHP5 and NPHP6 are always associated with progressive blindness. This clinical finding suggests that the gene products, nephrocystin-5 and nephrocystin-6, participate in overlapping signaling pathways to maintain photoreceptor homeostasis. To analyze the genetic interaction between these two proteins in more detail, we studied zebrafish embryos after depletion of NPHP5 and NPHP6. Knockdown of zebrafish zNPHP5 and zNPHP6 produced similar phenotypes, and synergistic effects were observed after the combined knockdown of zNPHP5 and zNPHP6. The N-terminal domain of nephrocystin-6-bound nephrocystin-5, and mapping studies delineated the interacting site from amino acid 696 to 896 of NPHP6. In Xenopus laevis, knockdown of NPHP5 caused substantial neural tube closure defects. This phenotype was copied by expression of the nephrocystin-5-binding fragment of nephrocystin-6, and rescued by co-expression of nephrocystin-5, supporting a physical interaction between both gene products in vivo. Since the N- and C-terminal fragments of nephrocystin-6 engage in the formation of homo- and heteromeric protein complexes, conformational changes seem to regulate the interaction of nephrocystin-6 with its binding partners.

Summary: Identifying mentions of named entities, such as genes or diseases, and normalizing them to database identifiers have become an important step in many text and data mining pipelines. Despite this need, very few entity normalization systems are publicly available as source code or web services for biomedical text mining. Here we present the Gnat Java library for text retrieval, named entity recognition, and normalization of gene and protein mentions in biomedical text. The library can be used as a component to be integrated with other text-mining systems, as a framework to add user-specific extensions, and as an efficient stand-alone application for the identification of gene and protein names for data analysis. On the BioCreative III test data, the current version of Gnat achieves a Tap-20 score of 0.1987.Availability: The library and web services are implemented in Java and the sources are available from http://gnat.sourceforge.net.Contact: jorg.hakenberg@roche.com