Abstract: Exon and exon+junction microarrays are promising tools for studying alternative splicing. Current analytical tools applied to these arrays lack two relevant features: the ability to predict unknown spliced forms and the ability to quantify the concentration of known and unknown isoforms. SPACE is an algorithm that has been developed to (1) estimate the number of different transcripts expressed under several conditions, (2) predict the precursor mRNA splicing structure and (3) quantify the transcript concentrations including unknown forms. The results presented here show its robustness and accuracy for real and simulated data.
Abstract: MOTIVATION: Eukaryotic genes are often regulated by multiple transcription factors (TFs). Depending on the interactions among different TFs the expression of a gene can be tuned to respond to diverse environmental conditions. Chip-on-chip experiments provide a snapshot of which TF are in vivo bound to which genes in a particular condition, and have been applied to characterize the regulatory code of yeast under several experimental settings. ChIPCodis mines this data to provide new insights about how the expression of a particular group of genes is regulated. For a given list of yeast genes ChIPCodis determines which combinations of TFs are significantly over-represented in a series of environmental conditions. Availability: http://chipcodis.dacya.ucm.es
Abstract: In the last few years, advances in high-throughput technologies are generating large amounts of biological data that require analysis and interpretation. Nonnegative matrix factorization (NMF) has been established as a very effective method to reveal information about the complex latent relationships in experimental data sets. Using this method as part of the exploratory data analysis, workflow would certainly help in the process of interpreting and understanding the complex biology mechanisms that are underlying experimental data. We have developed bioNMF, a web-based tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications in biology. This online tool provides a user-friendly interface, combined with a computational efficient parallel implementation of the NMF methods to explore the data in different analysis scenarios. In addition to the online access, bioNMF also provides the same functionality included in the website as a public web services interface, enabling users with more computer expertise to launch jobs into bioNMF server from their own scripts and workflows. bioNMF application is freely available at http://bionmf.dacya.ucm.es.
Abstract: We present GENECODIS, a web-based tool that integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of high-throughput experiments and may outperform the results of standard methods for the functional analysis of gene lists. GENECODIS is publicly available at http://genecodis.dacya.ucm.es/.
Abstract: BACKGROUND: Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. RESULTS: We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. CONCLUSION: The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.
Abstract: BACKGROUND: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. RESULTS: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work. CONCLUSION: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.
Abstract: BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at http://www.dacya.ucm.es/apascual/bioNMF.
Abstract: BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.
Abstract: BACKGROUND: Recent analyses in systems biology pursue the discovery of functional modules within the cell. Recognition of such modules requires the integrative analysis of genome-wide experimental data together with available functional schemes. In this line, methods to bridge the gap between the abstract definitions of cellular processes in current schemes and the interlinked nature of biological networks are required. RESULTS: This work explores the use of the scientific literature to establish potential relationships among cellular processes. To this end we have used a document based similarity method to compute pair-wise similarities of the biological processes described in the Gene Ontology (GO). The method has been applied to the biological processes annotated for the Saccharomyces cerevisiae genome. We compared our results with similarities obtained with two ontology-based metrics, as well as with gene product annotation relationships. We show that the literature-based metric conserves most direct ontological relationships, while reveals biologically sounded similarities that are not obtained using ontology-based metrics and/or genome annotation. CONCLUSION: The scientific literature is a valuable source of information from which to compute similarities among biological processes. The associations discovered by literature analysis are a valuable complement to those encoded in existing functional schemes, and those that arise by genome annotation. These similarities can be used to conveniently map the interlinked structure of cellular processes in a particular organism.
Abstract: The availability of oral precursors of 5-Fluorouracil (5-FU) and its favorable results in treating advanced breast cancer have renewed the interest in the molecular mechanisms underlying its cytotoxicity. We have compared the changes in cell cycle and cell death parameters induced by 2 different concentrations of 5-FU (IC50 and IC80) in the breast adenocarcinoma cell line MCF7. G1/S cell cycle arrest was associated with both concentrations, whereas cell death was mainly induced after IC80 5-FU. These changes were correlated with gene expression assessed by cDNA microarray analysis. Main findings included an overexpression of p53 target genes involved in cell cycle and apoptosis (CDKN1A/p21, TP53INP, TNFRSF6/FAS and BBC3/PUMA), and significant repression of Myc. High dose 5-FU also induced a higher regulation of the mitochondrial death genes APAF1, BAK1 and BCL2, and induction of genes of the ID family. Furthermore, we establish a direct causal relationship between p21, ID1 and ID2 overexpression, increased acetylation of histones H3 and H4 and binding of p53 to their promoters as a result of 5-FU treatment. The relevance of these findings was further studied after interfering p53 expression in MCF7 cells (shp53 cells), showing a lower induction of both, ID1 and ID2 transcripts, after 5-FU when compared with MCF7 shGFP control cells. This molecular characterization of dose- and time-dependent modifications of gene expression after 5-FU treatment should provide a resource for future basic studies addressing the molecular mechanisms of chemotherapy in breast cancer.
