Selection of informative clusters from hierarchical cluster tree with gene classes

BACKGROUND: A common clustering method in the analysis of gene expression data has been hierarchical clustering. Usually the analysis involves selection of clusters by cutting the tree at a suitable level and/or analysis of a sorted gene list that is obtained with the tree. Cutting of the hierarchical tree requires the selection of a suitable level and it results in the loss of information on the other level. Sorted gene lists depend on the sorting method of the joined clusters. Author proposes that the clusters should be selected using the gene classifications. RESULTS: This article presents a simple method for searching for clusters with the strongest enrichment of gene classes from a cluster tree. The clusters found are presented in the estimated order of importance. The method is demonstrated with a yeast gene expression data set and with two database classifications. The obtained clusters demonstrated a very strong enrichment of functional classes. The obtained clusters are also able to present similar gene groups to those that were observed from the data set in the original analysis and also many gene groups that were not reported in the original analysis. Visualization of the results on top of a cluster tree shows that the method finds informative clusters from several levels of the cluster tree and indicates that the clusters found could not have been obtained by simply cutting the cluster tree. Results were also used in the comparison of cluster trees from different clustering methods. CONCLUSION: The presented method should facilitate the exploratory analysis of big data sets when the associated categorical data is available

Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences

Author summary In the biosciences, predictive methods are becoming increasingly necessary as novel sequences are generated at an ever-increasing rate. The volume of sequence data necessitates Automated Function Prediction (AFP) as manual curation is often impossible. Unfortunately, selecting the best AFP method is complicated by researchers using different evaluation metrics. Furthermore, many commonly-used metrics can give misleading results. We argue that the use of poor metrics in AFP evaluation is a result of the lack of methods to benchmark the metrics themselves. We propose an approach called Artificial Dilution Series (ADS). ADS uses existing data sets to generate multiple artificial AFP results, where each result has a controlled error rate. We use ADS to understand whether different metrics can distinguish between results with known quantities of error. Our results highlight dramatic differences in performance between evaluation metrics. Automated protein annotation using the Gene Ontology (GO) plays an important role in the biosciences. Evaluation has always been considered central to developing novel annotation methods, but little attention has been paid to the evaluation metrics themselves. Evaluation metrics define how well an annotation method performs and allows for them to be ranked against one another. Unfortunately, most of these metrics were adopted from the machine learning literature without establishing whether they were appropriate for GO annotations. We propose a novel approach for comparing GO evaluation metrics called Artificial Dilution Series (ADS). Our approach uses existing annotation data to generate a series of annotation sets with different levels of correctness (referred to as their signal level). We calculate the evaluation metric being tested for each annotation set in the series, allowing us to identify whether it can separate different signal levels. Finally, we contrast these results with several false positive annotation sets, which are designed to expose systematic weaknesses in GO assessment. We compared 37 evaluation metrics for GO annotation using ADS and identified drastic differences between metrics. We show that some metrics struggle to differentiate between different signal levels, while others give erroneously high scores to the false positive data sets. Based on our findings, we provide guidelines on which evaluation metrics perform well with the Gene Ontology and propose improvements to several well-known evaluation metrics. In general, we argue that evaluation metrics should be tested for their performance and we provide software for this purpose (). ADS is applicable to other areas of science where the evaluation of prediction results is non-trivial.Peer reviewe

GOParGenPy: a high throughput method to generate Gene Ontology data matrices

Peer reviewe

Mlh1 deficiency in normal mouse colon mucosa associates with chromosomally unstable colon cancer

Colorectal cancer (CRC) genome is unstable and different types of instabilities, such as chromosomal instability (CIN) and microsatellite instability (MSI) are thought to reflect distinct cancer initiating mechanisms. Although 85% of sporadic CRC reveal CIN, 15% reveal mismatch repair (MMR) malfunction and MSI, the hallmarks of Lynch syndrome with inherited heterozygous germline mutations in MMR genes. Our study was designed to comprehensively follow genome-wide expression changes and their implications during colon tumorigenesis. We conducted a long-term feeding experiment in the mouse to address expression changes arising in histologically normal colonic mucosa as putative cancer preceding events, and the effect of inherited predisposition (Mlh1(+/-)) and Western-style diet (WD) on those. During the 21-month experiment, carcinomas developed mainly in WD-fed mice and were evenly distributed between genotypes. Unexpectedly, the heterozygote (B6.129-Mlh1tm1Rak) mice did not show MSI in their CRCs. Instead, both wildtype and heterozygote CRC mice showed a distinct mRNA expression profile and shortage of several chromosomal segregation gene-specific transcripts (Mlh1, Bub1, Mis18a, Tpx2, Rad9a, Pms2, Cenpe, Ncapd3, Odf2 and Dclre1b) in their colon mucosa, as well as an increased mitotic activity and abundant numbers of unbalanced/atypical mitoses in tumours. Our genome-wide expression profiling experiment demonstrates that cancer preceding changes are already seen in histologically normal colon mucosa and that decreased expressions of Mlh1 and other chromosomal segregation genes may form a field-defect in mucosa, which trigger MMR-proficient, chromosomally unstable CRC.Peer reviewe

Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering

Motivation: There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity

Epigenome-450K-wide methylation signatures of active cigarette smoking: The Young Finns Study

Smoking as a major risk factor for morbidity affects numerous regulatory systems of the human body including DNA methylation. Most of the previous studies with genome-wide methylation data are based on conventional association analysis and earliest threshold-based gene set analysis that lacks sensitivity to be able to reveal all the relevant effects of smoking. The aim of the present study was to investigate the impact of active smoking on DNA methylation at three biological levels: 5'-C-phosphate-G-3' (CpG) sites, genes and functionally related genes (gene sets). Gene set analysis was done with mGSZ, a modern threshold-free method previously developed by us that utilizes all the genes in the experiment and their differential methylation scores. Application of such method in DNA methylation study is novel. Epigenome-wide methylation levels were profiled from Young Finns Study (YFS) participants' whole blood from 2011 follow-up using Illumina Infinium HumanMethylation450 BeadChips. We identified three novel smoking related CpG sites and replicated 57 of the previously identified ones. We found that smoking is associated with hypomethylation in shore (genomic regions 0-2 kilobases from CpG island). We identified smoking related methylation changes in 13 gene sets with false discovery rate (FDR) <= 0.05, among which is olfactory receptor activity, the flagship novel finding of the present study. Overall, we extended the current knowledge by identifying: (i) three novel smoking related CpG sites, (ii) similar effects as aging on average methylation in shore, and (iii) a novel finding that olfactory receptor activity pathway responds to tobacco smoke and toxin exposure through epigenetic mechanisms

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set

PlasmoDraft: a database of Plasmodium falciparum gene function predictions based on postgenomic data

<p>Abstract</p> <p>Background</p> <p>Of the 5 484 predicted proteins of <it>Plasmodium falciparum</it>, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes.</p> <p>Results</p> <p>We present PlasmoDraft <url>http://atgc.lirmm.fr/PlasmoDraft/</url>, a database of Gene Ontology (GO) annotation predictions for <it>P. falciparum </it>genes based on postgenomic data. Predictions of PlasmoDraft are achieved with a <it>Guilt By Association </it>method named Gonna. This involves (1) a predictor that proposes GO annotations for a gene based on the similarity of its profile (measured with transcriptome, proteome or interactome data) with genes already annotated by GeneDB; (2) a procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines all data sources to provide a global summary and confidence estimate of the predictions. Gonna has been applied to all <it>P. falciparum </it>genes using most publicly available transcriptome, proteome and interactome data sources. Gonna provides predictions for numerous genes without any annotations. For example, 2 434 genes without any annotations in the Biological Process ontology are associated with specific GO terms (<it>e.g</it>. Rosetting, Antigenic variation), and among these, 841 have confidence values above 50%. In the Cellular Component and Molecular Function ontologies, 1 905 and 1 540 uncharacterized genes are associated with specific GO terms, respectively (740 and 329 with confidence value above 50%).</p> <p>Conclusion</p> <p>All predictions along with their confidence values have been compiled in PlasmoDraft, which thus provides an extensive database of GO annotation predictions that can be achieved with these data sources. The database can be accessed in different ways. A global view allows for a quick inspection of the GO terms that are predicted with high confidence, depending on the various data sources. A gene view and a GO term view allow for the search of potential GO terms attached to a given gene, and genes that potentially belong to a given GO term.</p

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

<p>Abstract</p> <p>Background</p> <p>Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.</p> <p>Results</p> <p>Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.</p> <p>Conclusion</p> <p>Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.</p

Digital Gene Expression Profiling by 5′-End Sequencing of cDNAs during Reprogramming in the Moss Physcomitrella patens

Stem cells self-renew and repeatedly produce differentiated cells during development and growth. The differentiated cells can be converted into stem cells in some metazoans and land plants with appropriate treatments. After leaves of the moss Physcomitrella patens are excised, leaf cells reenter the cell cycle and commence tip growth, which is characteristic of stem cells called chloronema apical cells. To understand the underlying molecular mechanisms, a digital gene expression profiling method using mRNA 5′-end tags (5′-DGE) was established. The 5′-DGE method produced reproducible data with a dynamic range of four orders that correlated well with qRT-PCR measurements. After the excision of leaves, the expression levels of 11% of the transcripts changed significantly within 6 h. Genes involved in stress responses and proteolysis were induced and those involved in metabolism, including photosynthesis, were reduced. The later processes of reprogramming involved photosynthesis recovery and higher macromolecule biosynthesis, including of RNA and proteins. Auxin and cytokinin signaling pathways, which are activated during stem cell formation via callus in flowering plants, are also activated during reprogramming in P. patens, although no exogenous phytohormone is applied in the moss system, suggesting that an intrinsic phytohormone regulatory system may be used in the moss