SQUAT: A web tool to mine human, murine and avian SAGE data

International audienceBACKGROUND: There is an increasing need in transcriptome research for gene expression data and pattern warehouses. It is of importance to integrate in these warehouses both raw transcriptomic data, as well as some properties encoded in these data, like local patterns. DESCRIPTION: We have developed an application called SQUAT (SAGE Querying and Analysis Tools) which is available at: http://bsmc.insa-lyon.fr/squat/. This database gives access to both raw SAGE data and patterns mined from these data, for three species (human, mouse and chicken). This database allows to make simple queries like "In which biological situations is my favorite gene expressed?" as well as much more complex queries like: . Connections with external web databases enrich biological interpretations, and enable sophisticated queries. To illustrate the power of SQUAT, we show and analyze the results of three different queries, one of which led to a biological hypothesis that was experimentally validated. CONCLUSION: SQUAT is a user-friendly information retrieval platform, which aims at bringing some of the state-of-the-art mining tools to biologists

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation

Knowledge discovery through creating formal contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called Formal Concept Analysis (FCA). This paper describes a tool called FcaBedrock that converts data into Formal Contexts for FCA. The paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Creating Formal Contexts using FcaBedrock is shown to be straightforward and versatile. Large data sets are easily converted into a standard FCA format

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

Formal Concept Analysis Applications in Bioinformatics

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed. Adviser: Jitender S. Deogu