MarVis: a tool for clustering and visualization of metabolic biomarkers

Background

Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.

Results

We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.

Conclusion

By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.     

How to decide which are the most pertinent overly-represented features during gene set enrichment analysis

Background  

The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.     

Analysis of gene ontology features in microarray data using the Proteome BioKnowledge Library

Microarray technology has resulted in an explosion of complex, valuable data. Integrating data analysis tools with a comprehensive underlying database would allow efficient identification of common properties among differentially regulated genes. In this study we sought to compare the utility of various databases in microarray analysis. The Proteome BioKnowledge Library (BKL), a manually curated, proteome-wide compilation of the scientific literature, was used to generate a list of Gene Ontology (GO) Biological Process (BP) terms enriched among proteins involved in cardiovascular disease. Analysis of DNA microarray data generated in a study of rat vascular smooth muscle cell responses revealed significant enrichment in a number of GO BPs that were also enriched among cardiovascular disease-related proteins. Using annotation from LocusLink and chip annotation from the Gene Expression Omnibus yielded fewer enriched cardiovascular disease-associated GO BP terms. Data sets of orthologous genes from mouse and human were generated using the BKL Retriever. Analysis of these sets focusing on BKL Disease annotation, revealed a significant association of these genes with cardiovascular disease. These results and the extensive presence of experimental evidence for BKL GO and Disease features, underscore the benefits of using this database for microarray analysis.     

Analysis of a Gibbs sampler method for model-based clustering of gene expression data

MOTIVATION: Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. RESULTS: We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. AVAILABILITY: GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software     

Functional annotation of genes overlapping copy number variants in autistic patients: focus on axon pathfinding

We have used Gene Ontology (GO) and pathway analyses to uncover the common functions associated to the genes overlapping Copy Number Variants (CNVs) in autistic patients. Our source of data were four published studies [1-4]. We first applied a two-step enrichment strategy for autism-specific genes. We fished out from the four mentioned studies a list of 2928 genes overall overlapping 328 CNVs in patients and we first selected a sub-group of 2044 genes after excluding those ones that are also involved in CNVs reported in the Database of Genomic Variants (enrichment step 1). We then selected from the step 1-enriched list a sub-group of 514 genes each of which was found to be deleted or duplicated in at least two patients (enrichment step 2). The number of statistically significant processes and pathways identified by the Database for Annotation, Visualization and Integrated Discovery and Ingenuity Pathways Analysis softwares with the step 2-enriched list was significantly higher compared to the step 1-enriched list. In addition, statistically significant GO terms, biofunctions and pathways related to nervous system development and function were exclusively identified by the step 2-enriched list of genes. Interestingly, 21 genes were associated to axon growth and pathfinding. The latter genes and other ones associated to nervous system in this study represent a new set of autism candidate genes deserving further investigation. In summary, our results suggest that the autism's "connectivity genes" in some patients affect very early phases of neurodevelopment, i.e., earlier than synaptogenesis.     

GOCompare: An R package to compare functional enrichment analysis between two species

Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.     

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.     

Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including ‘Sphingoid metabolic process,’ ‘Ubiquitin protein ligase activity,’ ‘Regulation of cytokine secretion,’ and ‘Ceramide metabolic process.’ These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data.     

Identifying splits with clear separation: a new class discovery method for gene expression data

We present a new class discovery method for microarray gene expression data. Based on a collection of gene expression profiles from different tissue samples, the method searches for binary class distinctions in the set of samples that show clear separation in the expression levels of specific subsets of genes. Several mutually independent class distinctions may be found, which is difficult to obtain from most commonly used clustering algorithms. Each class distinction can be biologically interpreted in terms of its supporting genes. The mathematical characterization of the favored class distinctions is based on statistical concepts. By analyzing three data sets from cancer gene expression studies, we demonstrate that our method is able to detect biologically relevant structures, for example cancer subtypes, in an unsupervised fashion.     

Establishment of optimal regulatory network of colorectal cancer based on p42.3 protein

Objective: to establish regulatory network of colorectal cancer involving p42.3 protein and to provide theoretical evidence for deep functional exploration of p42.3 protein in the onset and development of colorectal cancer. Methods: with protein similarity algorithm, reference protein set of p42.3 cell apoptosis was built according to structural features of p42.3. GO and KEGG databases were used to establish regulatory network of tumor cell apoptosis involving p42.3; meanwhile, the largest possible working pathway that involves p42.3 protein was screened out based on Bayesian network theory. Besides, GO and KEGG were used to build regulatory network on early diagnosis gene markers for colorectal cancer including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, at the same time, a regulatory network of colorectal cancer cell apoptosis which involves p42.3 was established. Results: cell apoptotic regulatory network that p42.3 participates in primarily consists of Bcl-2 family genes and the largest possible pathway is p42.3 → FKBP → Bcl-2 centered as FKBP protein. Combined with colorectal cancer regulatory network that involves early diagnosis gene markers, it can be predicted that p42.3 is most likely to regulate the colorectal cancer cell apoptosis through FKBP → Bcl-2 → Bax → caspase-9 → caspase-3 pathway. Conclusion: the colorectal cancer apoptosis network based on p42.3 established in the study provides theoretical evidence for deep exploration of p42.3 regulatory mechanism and molecular targeting treatment of colorectal cancer.     

Correlation between gene expression and GO semantic similarity

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their gene ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in gene ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.     

A framework for incorporating functional interrelationships into protein function prediction algorithms

The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many computational approaches have been developed in recent years to predict protein function, most of these traditional algorithms do not take interrelationships among functional terms into account, such as different GO terms usually coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large size ones when considering functional interrelationships. We also compare our similarity measure with other two widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our method is robust to annotations in the database which are not complete at present. These results give new insights about the importance of functional interrelationships in protein function prediction.     

ADGO: analysis of differentially expressed gene sets using composite GO annotation

MOTIVATION: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. RESULTS: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. AVAILABILITY: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. CONTACT: chu@kribb.re.kr SUPPLEMENTARY INFORMATION: http://array.kobic.re.kr/ADGO.     

Which Is a More Accurate Predictor in Colorectal Survival Analysis? Nine Data Mining Algorithms vs. the TNM Staging System

Objective

Over the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis.

Methods

Two different datasets were used: 1) the National Cancer Institute''s Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7^th edition of the American Joint Committee on Cancer TNM staging system.

Results

When the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, Bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05).

Conclusions

The TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.     

A critical assessment of <Emphasis Type="Italic">Mus musculus</Emphasis>gene function prediction using integrated genomic evidence

Background:

Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.

Results:

In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.

Conclusion:

We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

     

Redundancy in genotyping arrays

Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes.     

Using whole genome presence/absence data to untangle function in 12 Drosophila genomes

The Drosophila 12 genome data set was used to construct whole genome, gene family presence/absence matrices using a broad range of E value cutoffs as criteria for gene family inclusion. The various matrices generated behave differently in phylogenetic analyses as a function of the e-value employed. Based on an optimality criterion that maximizes internal corroboration of information, we show that values of e-105 to e-125 extract the most internally consistent phylogenetic signal. Functional class of most genes and gene families can be accurately determined based on the D. melanogaster genome annotation. We used the gene ontology (GO) system to create partitions based on gene function. Several measures of phylogenetic congruence (diagnosis, consistency, partitioned support , hidden support) for different higher and lower level GO categories, were used to mine the data set for genes and gene families that show strong agreement or disagreement with the overall combined phylogenetic hypothesis. We propose that measures of phylogenetic congruence can be used as criteria to identify loci with related GO terms that have a significant impact on cladogenesis.     

Consistent probabilistic outputs for protein function prediction

In predicting hierarchical protein function annotations, such as terms in the Gene Ontology (GO), the simplest approach makes predictions for each term independently. However, this approach has the unfortunate consequence that the predictor may assign to a single protein a set of terms that are inconsistent with one another; for example, the predictor may assign a specific GO term to a given protein ('purine nucleotide binding') but not assign the parent term ('nucleotide binding'). Such predictions are difficult to interpret. In this work, we focus on methods for calibrating and combining independent predictions to obtain a set of probabilistic predictions that are consistent with the topology of the ontology. We call this procedure 'reconciliation'. We begin with a baseline method for predicting GO terms from a collection of data types using an ensemble of discriminative classifiers. We apply the method to a previously described benchmark data set, and we demonstrate that the resulting predictions are frequently inconsistent with the topology of the GO. We then consider 11 distinct reconciliation methods: three heuristic methods; four variants of a Bayesian network; an extension of logistic regression to the structured case; and three novel projection methods - isotonic regression and two variants of a Kullback-Leibler projection method. We evaluate each method in three different modes - per term, per protein and joint - corresponding to three types of prediction tasks. Although the principal goal of reconciliation is interpretability, it is important to assess whether interpretability comes at a cost in terms of precision and recall. Indeed, we find that many apparently reasonable reconciliation methods yield reconciled probabilities with significantly lower precision than the original, unreconciled estimates. On the other hand, we find that isotonic regression usually performs better than the underlying, unreconciled method, and almost never performs worse; isotonic regression appears to be able to use the constraints from the GO network to its advantage. An exception to this rule is the high precision regime for joint evaluation, where Kullback-Leibler projection yields the best performance.