Dataset Information

GeneTopics--interpretation of gene sets via literature-driven topic models.

ABSTRACT:

Background

Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.

Methods

Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research.

Results

We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner.

Conclusions

Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets.

SUBMITTER: Wang V

PROVIDER: S-EPMC4029197 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

GeneTopics--interpretation of gene sets via literature-driven topic models.

Wang Vicky V Xi Li L Enayetallah Ahmed A Fauman Eric E Ziemek Daniel D

BMC systems biology 20131209

<h4>Background</h4>Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstru ...[more]

PMID: 24564875

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Project description:BackgroundHigh-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional gene/protein group interpretation are expensive and time-consuming; therefore, there is a need to develop new efficient data mining methods and bioinformatics tools that could support the expert in the process of functional analysis of experimental results.ResultsIn this study, we propose a comprehensive framework for the induction of logical rules in the form of combinations of Gene Ontology (GO) terms for functional interpretation of gene sets. Within the framework, we present four approaches: the fully automated method of rule induction without filtering, rule induction method with filtering, expert-driven rule filtering method based on additive utility functions, and expert-driven rule induction method based on the so-called seed or expert terms - the GO terms of special interest which should be included into the description. These GO terms usually describe some processes or pathways of particular interest, which are related to the experiment that is being performed. During the rule induction and filtering processes such seed terms are used as a base on which the description is build.ConclusionWe compare the descriptions obtained with different algorithms of rule induction and filtering and show that a filtering step is required to reduce the number of rules in the output set so that they could be analyzed by a human expert. However, filtering may remove information from the output rule set which is potentially interesting for the expert. Therefore, in the study, we present two methods that involve interaction with the expert during the process of rule induction. Both of them are able to reduce the number of rules, but only in the case of the method based on seed terms, each of the created rule includes expert terms in combination with the other terms. Further analysis of such combinations may provide new knowledge about biological processes and their combination with other pathways related to genes described by the rules. A suite of Matlab scripts that provide the functionality of a comprehensive framework for the rule induction and filtering presented in this study is available free of charge at: http://rulego.polsl.pl/framework .

Project description:BACKGROUND: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. METHODS: We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. RESULTS: Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. CONCLUSIONS: Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

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Dataset Information

GeneTopics--interpretation of gene sets via literature-driven topic models.

Background

Methods

Results

Conclusions

Publications

GeneTopics--interpretation of gene sets via literature-driven topic models.

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