Dataset Information

Automatic assignment of prokaryotic genes to functional categories using literature profiling.

ABSTRACT: In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functional category despite the fact that there is enough evidence in the literature to predict their function. We developed a classifier trained with term-frequency vectors automatically disclosed from text corpora of an ensemble of genes representative of each functional category of the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR) ontology. The classifier achieved up to 84% precision with 68% recall (for confidence?0.4), F-measure 0.76 (recall and precision equally weighted) in an independent set of 2,220 genes, from 13 bacterial species, previously classified by JCVI-CMR into unambiguous categories of its ontology. Finally, the classifier assigned (confidence?0.7) to functional categories a total of 5,235 out of the ?24 thousand genes previously in categories "Unknown function" or "Unclassified" for which there is literature in MEDLINE. Two biologists reviewed the literature of 100 of these genes, randomly picket, and assigned them to the same functional categories predicted by the automatic classifier. Our results confirmed the hypothesis that it is possible to confidently assign genes of a real world repository to functional categories, based exclusively on the automatic profiling of its associated literature. The LitProf--Gene Classifier web server is accessible at: www.cebio.org/litprofGC.

SUBMITTER: Torrieri R

PROVIDER: S-EPMC3471813 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Automatic assignment of prokaryotic genes to functional categories using literature profiling.

Torrieri Raul R Oliveira Francislon S FS Oliveira Guilherme G Coimbra Roney S RS

PloS one 20121015 10

In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functi ...[more]

PMID: 23077617

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Project description:BackgroundRetrieving pertinent information from biological scientific literature requires cutting-edge text mining methods which may be able to recognize the meaning of the very ambiguous names of biological entities. Aliases of a gene share a common vocabulary in their respective collections of PubMed abstracts. This may be true even when these aliases are not associated with the same subset of documents. This gene-specific vocabulary defines a unique fingerprint that can be used to disclose ambiguous aliases. The present work describes an original method for automatically assessing the ambiguity levels of gene aliases in large gene terminologies based exclusively in the content of their associated literature. The method can deal with the two major problems restricting the usage of current text mining tools: 1) different names associated with the same gene; and 2) one name associated with multiple genes, or even with non-gene entities. Important, this method does not require training examples.ResultsAliases were considered "ambiguous" when their Jaccard distance to the respective official gene symbol was equal or greater than the smallest distance between the official gene symbol and one of the three internal controls (randomly picked unrelated official gene symbols). Otherwise, they were assigned the status of "synonyms". We evaluated the coherence of the results by comparing the frequencies of the official gene symbols in the text corpora retrieved with their respective "synonyms" or "ambiguous" aliases. Official gene symbols were mentioned in the abstract collections of 42 % (70/165) of their respective synonyms. No official gene symbol occurred in the abstract collections of any of their respective ambiguous aliases. In overall, querying PubMed with official gene symbols and "synonym" aliases allowed a 3.6-fold increase in the number of unique documents retrieved.ConclusionsThese results confirm that this method is able to distinguish between synonyms and ambiguous gene aliases based exclusively on their vocabulary fingerprint. The approach we describe could be used to enhance the retrieval of relevant literature related to a gene.

Project description:Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.

Dataset Information

Automatic assignment of prokaryotic genes to functional categories using literature profiling.

Publications

Automatic assignment of prokaryotic genes to functional categories using literature profiling.

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