Kevin Heinrich

Gene clustering by Latent Semantic Indexing of MEDLINE abstracts

MOTIVATION A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. RESULTS We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. AVAILABILITY The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.

Bioinformatic Analysis Reveals cRel as a Regulator of a Subset of Interferon-Stimulated Genes

Interferons (IFNs) are critical to the host innate immune response by inducing the expression of a family of early response genes, denoted as IFN-stimulated genes (ISGs). The role of tyrosine phosphorylation of STAT proteins in the transcription activation of ISGs is well-documented. Recent studies have indicated that other transcription factors (TFs) are likely to play a role in regulating ISG expression. Here, we describe a novel integrative approach that combines gene expression profiling, promoter sequence analysis, and literature mining to screen candidate regulatory factors in the IFN signal transduction pathway. Application of this method identified the nuclear factor kappaB (NFkappaB) protein, cRel, as a candidate regulatory factor for a subset of ISGs in mouse embryo fibroblasts. Chromatin immunoprecipitation (ChIP) and real-time PCR assays confirmed that cRel directly binds to the promoters of several ISGs, including Cxcl10, Isg15, Gbp2, Ifit3, and Ifi203, and regulates their expression. Thus, our studies identify cRel as an important TF for ISGs, and validate the approach of using Latent Semantic Indexing (LSI)-based methods to identify regulatory factors from microarray data.

Functional Cohesion of Gene Sets Determined by Latent Semantic Indexing of PubMed Abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fishers exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability GCAT is freely available at http://binf1.memphis.edu/gcat

Latent Semantic Indexing of PubMed abstracts for identification of transcription factor candidates from microarray derived gene sets

BackgroundIdentification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for identification of putative regulatory TFs that might be explicitly or implicitly linked to the genes under investigation.ResultsIn this study, we present a Latent Semantic Indexing (LSI) based text mining approach for identification and ranking of putative regulatory TFs from microarray derived differentially expressed genes (DEGs). Two LSI models were built using different term weighting schemes to devise pair-wise similarities between 21,027 mouse genes annotated in the Entrez Gene repository. Amongst these genes, 433 were designated TFs in the TRANSFAC database. The LSI derived TF-to-gene similarities were used to calculate TF literature enrichment p-values and rank the TFs for a given set of genes. We evaluated our approach using five different publicly available microarray datasets focusing on TFs Rel, Stat6, Ddit3, Stat5 and Nfic. In addition, for each of the datasets, we constructed gold standard TFs known to be functionally relevant to the study in question. Receiver Operating Characteristics (ROC) curves showed that the log-entropy LSI model outperformed the tf-normal LSI model and a benchmark co-occurrence based method for four out of five datasets, as well as motif searching approaches, in identifying putative TFs.ConclusionsOur results suggest that our LSI based text mining approach can complement existing approaches used in systems biology research to decipher gene regulatory networks by providing putative lists of ranked TFs that might be explicitly or implicitly associated with sets of DEGs derived from microarray experiments. In addition, unlike motif searching approaches, LSI based approaches can reveal TFs that may indirectly regulate genes.

Gene tree labeling using nonnegative matrix factorization on biomedical literature

Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters and initialization, to provide an automated way to classify biomedical data, and to provide a method for evaluating labeled data assuming a static input tree. As a byproduct, a method for generating gold standard trees is proposed.

Using a literature-based NMF model for discovering gene functional relationships

The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software environment called FAUN or feature annotation using nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation.

Gene-set Cohesion Analysis Tool (GCAT): A literature based web tool for calculating functional cohesiveness of gene groups

Numerous algorithms exist for producing gene sets from high-throughput genomic and proteomic technologies. However, analysis of the functional significance of these groups of genes or proteins remains a big challenge. We developed a web based system called Gene-set cohesion analysis tool (GCAT) for estimating the significance level of the functional cohesion of a given gene set. The method utilizes Latent Semantic Indexing (LSI) derived gene-gene literature similarities to determine if the functional coherence of a gene set is statistically significant compared to that expected by chance. The robustness of the method was determined by evaluating the functional cohesion for over 6000 Gene Ontology categories. Here, we demonstrate the utility of GCAT for analysis of microarray data from previously published experiments in which embryonic fibroblasts were treated with interferon. Using GCAT, we found the highest literature cohesion p-value (p= 1.37E-63) corresponded to a set of genes that were differentially regulated > 2-fold and had a t-test p-value <0.05, compared to genes that were only changed >2-fold (literature p-value=2.2E-44) or had a p-value <0.05 (literature p-value=6.0E-32). As a control, genes that were changed less than 2-fold or had a p-value >0.05 did not show a significant literature cohesion. These results demonstrate that GCAT can provide an objective literature-based measure to evaluate the biological significance of gene sets identified by different criterions. GCAT is available at http://motif.memphis.edu/gcat/.