Dataset Information

Annotation and cross-indexing of array elements on multiple platforms.

ABSTRACT: On the surface, transcript profiling using microarrays seems to offer a way of looking at the global response of the cell to perturbation, with a focus on changes in gene expression. The difficulty, however, is that the response of a particular gene is actually measured on the array by an element that is a short, defined nucleic acid sequence. Sequences that map back to the same genetic locus may actually be given different names and descriptions when they are deposited in public sequence databases; when such sequences are used in microarray construction, elements that monitor the same genetic locus may have different names and descriptions. The algorithm described here uses a hierarchical approach to assign a single best annotation to the elements in a given microarray in such a fashion that elements from one microarray platform may be cross-indexed with those of another. The algorithm relies on the nucleic acid accession number for a given array element, and uses that to retrieve annotation from the most recent versions of LocusLink and UniGene. Both database resources are searched, with a priority being given to annotation derived from the curated LocusLink database. In lieu of annotation found in these databases, the default GenBank annotation is used. As a final outcome, a cross-chip identifier is generated that may be used to cross-index array elements. The program is available as a practical extraction and report language (Perl) script that can run under any Perl interpreter.

SUBMITTER: Mattes WB

PROVIDER: S-EPMC1241905 | biostudies-other | 2004 Mar

REPOSITORIES: biostudies-other

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Annotation and cross-indexing of array elements on multiple platforms.

Mattes William B WB

Environmental health perspectives 20040301 4

On the surface, transcript profiling using microarrays seems to offer a way of looking at the global response of the cell to perturbation, with a focus on changes in gene expression. The difficulty, however, is that the response of a particular gene is actually measured on the array by an element that is a short, defined nucleic acid sequence. Sequences that map back to the same genetic locus may actually be given different names and descriptions when they are deposited in public sequence databa ...[more]

PMID: 15033601

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Project description:Abstract The automatic recognition of chemical names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The task is even more challenging when considering the identification of these entities in the article’s full text and, furthermore, the identification of candidate substances for that article’s metadata [Medical Subject Heading (MeSH) article indexing]. The National Library of Medicine (NLM)-Chem track at BioCreative VII aimed to foster the development of algorithms that can predict with high quality the chemical entities in the biomedical literature and further identify the chemical substances that are candidates for article indexing. As a result of this challenge, the NLM-Chem track produced two comprehensive, manually curated corpora annotated with chemical entities and indexed with chemical substances: the chemical identification corpus and the chemical indexing corpus. The NLM-Chem BioCreative VII (NLM-Chem-BC7) Chemical Identification corpus consists of 204 full-text PubMed Central (PMC) articles, fully annotated for chemical entities by 12 NLM indexers for both span (i.e. named entity recognition) and normalization (i.e. entity linking) using MeSH. This resource was used for the training and testing of the Chemical Identification task to evaluate the accuracy of algorithms in predicting chemicals mentioned in recently published full-text articles. The NLM-Chem-BC7 Chemical Indexing corpus consists of 1333 recently published PMC articles, equipped with chemical substance indexing by manual experts at the NLM. This resource was used for the evaluation of the Chemical Indexing task, which evaluated the accuracy of algorithms in predicting the chemicals that should be indexed, i.e. appear in the listing of MeSH terms for the document. This set was further enriched after the challenge in two ways: (i) 11 NLM indexers manually verified each of the candidate terms appearing in the prediction results of the challenge participants, but not in the MeSH indexing, and the chemical indexing terms appearing in the MeSH indexing list, but not in the prediction results, and (ii) the challenge organizers algorithmically merged the chemical entity annotations in the full text for all predicted chemical entities and used a statistical approach to keep those with the highest degree of confidence. As a result, the NLM-Chem-BC7 Chemical Indexing corpus is a gold-standard corpus for chemical indexing of journal articles and a silver-standard corpus for chemical entity identification in full-text journal articles. Together, these resources are currently the most comprehensive resources for chemical entity recognition, and we demonstrate improvements in the chemical entity recognition algorithms. We detail the characteristics of these novel resources and make them available for the community. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/NLM-Chem-BC7-corpus/

Dataset Information

Annotation and cross-indexing of array elements on multiple platforms.

Publications

Annotation and cross-indexing of array elements on multiple platforms.

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