Showing papers in "Briefings in Bioinformatics in 2007"
TL;DR: This article intends to present the basics of the Petri nets approach and to foster the potential role PNs could play in the development of the computational systems biology.
Abstract: Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.
404 citations
TL;DR: The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used.
Abstract: The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.
301 citations
TL;DR: The current state of the art in biomedical text mining or 'BioNLP' in general is reviewed, focusing primarily on papers published within the past year.
Abstract: It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.
291 citations
TL;DR: The diversity of innovative approaches to identify and annotate TEs in the post-genomic era is reviewed, covering both the discovery of new TE families and the detection of individual TE copies in genome sequences.
Abstract: The contribution of transposable elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis. Here we review the diversity of innovative approaches to identify and annotate TEs in the post-genomic era, covering both the discovery of new TE families and the detection of individual TE copies in genome sequences. These approaches span a broad spectrum in computational biology including de novo, homology-based, structure-based and comparative genomic methods. We conclude that the integration and visualization of multiple approaches and the development of new conceptual representations for TE annotation will further advance the computational analysis of this dynamic component of the genome.
210 citations
TL;DR: In this review, the work involved in preparing for the construction of BBMRI in a European and global context is described.
Abstract: Biobanks are well-organized resources comprising biological samples and associated information that are accessible to scientific investigation. Across Europe, millions of samples with related data are held in different types of collections. While individual collections can be well organized and accessible, the resources are subject to fragmentation, insecurity of funding and incompleteness. To address these issues, a Biobanking and BioMolecular Resources Infrastructure (BBMRI) is to be developed across Europe, thereby implementing a European 'roadmap' for research infrastructures that was developed by a forum of EU member states and that has been received by the European Commission. In this review, we describe the work involved in preparing for the construction of BBMRI in a European and global context.
208 citations
TL;DR: This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics.
Abstract: Being a relatively new addition to the 'omics' field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.
199 citations
TL;DR: This review explores recent approaches to the understanding of the mechanisms of disease at the molecular level through their underlying protein interactions through genetics, protein structure and protein interaction network analyses.
Abstract: The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the genomic data associated with diseases, however, creates a new challenge. Understanding the complex interplay between genes and proteins requires integration of data from a wide variety of sources, i.e. gene expression, genetic linkage, protein interaction, and protein structure among others. Thus, computational tools have become critical for the integration, representation and visualization of heterogeneous biomedical data. Furthermore, several bioinformatics methods have been developed to formulate predictions about the functional role of genes and proteins, including their role in diseases. After an introduction to the complex interplay between proteins and genetic diseases, this review explores recent approaches to the understanding of the mechanisms of disease at the molecular level. Finally, because most known mechanisms leading to disease involve some form of protein interaction, this review focuses on the recent methodologies for understanding diseases through their underlying protein interactions. Recent contributions from genetics, protein structure and protein interaction network analyses to the understanding of diseases are discussed here.
192 citations
TL;DR: An introduction to ABM is provided as it has been used to study complex multi-cell biological phenomena, the importance of coupling models with experimental work is underscore, and future challenges for the ABM field and its application to biomedical research are outlined.
Abstract: Agent-based modeling (ABM), also termed ‘Individual-based modeling (IBM)’, is a computational approach that simulates the interactions of autonomous entities (agents, or individual cells) with each other and their local environment to predict higher level emergent patterns. A literature-derived rule set governs the actions of each individual agent. While this technique has been widely used in the ecological and social sciences, it has only recently been applied in biomedical research. The purpose of this review is to provide an introduction to ABM as it has been used to study complex multi-cell biological phenomena, underscore the importance of coupling models with experimental work, and outline future challenges for the ABM field and its application to biomedical research. We highlight a number of published examples of ABM, focusing on work that has combined experimental with ABM analyses and how this pairing produces new understanding. We conclude with suggestions for moving forward with this parallel approach.
150 citations
TL;DR: This article reviews dimensionality reduction methods that have been used in proteomic biomarker studies, and points out the potential of novel dimension reduction techniques, in particular those that incorporate domain knowledge through the use of informative priors or causal inference.
Abstract: Mass-spectra based proteomic profiles have received widespread attention as potential tools for biomarker discovery and early disease diagnosis. A major data-analytical problem involved is the extremely high dimensionality (i.e. number of features or variables) of proteomic data, in particular when the sample size is small. This article reviews dimensionality reduction methods that have been used in proteomic biomarker studies. It then focuses on the problem of selecting the most appropriate method for a specific task or dataset, and proposes method combination as a potential alternative to single-method selection. Finally, it points out the potential of novel dimension reduction techniques, in particular those that incorporate domain knowledge through the use of informative priors or causal inference.
148 citations
TL;DR: Positive circuits are involved in the generation of multiple differentiated states, whereas negative circuits can generate cyclic or homeostatic behaviours, further encompassing their application to the design of synthetic regulatory systems.
Abstract: Regulatory circuits are found at the basis of all non-trivial dynamical properties of biological networks. More specifically, positive circuits are involved in the generation of multiple differentiated states, whereas negative circuits can generate cyclic or homeostatic behaviours. These notions are briefly reviewed, from initial biological formulations to mathematical formalisations, further encompassing their application to the design of synthetic regulatory systems. Finally, current challenges for the analysis of increasingly complex regulatory networks are indicated, as well as prospects for our understanding of development and evolution.
108 citations
TL;DR: Different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data are discussed and the procedure for computationally recovering the quantitative information is described.
Abstract: Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC n -MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC n -MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.
TL;DR: This review explains how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling and shows how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias.
Abstract: The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.
TL;DR: Methods for the detection of correlated amino acid substitutions are reviewed, their relative performance in contact prediction is compared and future directions in the field are predicted.
Abstract: It has long been suspected that analysis of correlated amino acid substitutions should uncover pairs or clusters of sites that are spatially proximal in mature protein structures. Accordingly, methods based on different mathematical principles such as information theory, correlation coefficients and maximum likelihood have been developed to identify co-evolving amino acids from multiple sequence alignments. Sets of pairs of sites whose behaviour is identified by these methods as correlated are often significantly enriched in pairs of spatially proximal residues. However, relatively high levels of false-positive predictions typically render such methods, in isolation, of little use in the ab initio prediction of protein structure. Misleading signal (or problems with the estimation of significance levels) can be caused by phylogenetic correlations between homologous sequences and from correlation due to factors other than spatial proximity (for example, correlation of sites which are not spatially close but which are involved in common functional properties of the protein). In recent years, several workers have suggested that information from correlated substitutions should be combined with other sources of information (secondary structure, solvent accessibility, evolutionary rates) in an attempt to reduce the proportion of false-positive predictions. We review methods for the detection of correlated amino acid substitutions, compare their relative performance in contact prediction and predict future directions in the field.
TL;DR: Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread.
Abstract: Biological knowledge can be inferred from three major levels of information: molecules, organisms and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data; biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key ‘model’ organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal metadata element to link and integrate biodiversity data across a range of data sources.
TL;DR: Two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map are proposed, both prototype-based such that the principle of characteristic representants is realized.
Abstract: In the present contribution we propose two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both prototype-based such that the principle of characteristic representants is realized. This leads to an easy interpretation of the generated classifcation model. Further, the fuzzy-labeled self-organizing map is able to process uncertainty in data, and classification results can be obtained as fuzzy decisions. Moreover, this fuzzy classification together with the property of topographic mapping offers the possibility of class similarity detection, which can be used for class visualization. We demonstrate the power of both methods for two exemplary examples: the classification of bacteria (listeria types) and neoplastic and non-neoplastic cell populations in breast cancer tissue sections.
TL;DR: A methodology for data integration in biomedical research that is based on EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) is reviewed.
Abstract: Data integration is needed in order to cope with the huge amounts of biological information now available and to perform data mining effectively. Current data integration systems have strict limitations, mainly due to the number of resources, their size and frequency of updates, their heterogeneity and distribution on the Internet. Integration must therefore be achieved by accessing network services through flexible and extensible data integration and analysis network tools. EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) can support the creation and deployment of such systems. Many XML languages and Web Services for bioinformatics have already been designed and implemented and some WMS have been proposed. In this article, we review a methodology for data integration in biomedical research that is based on these technologies. We also briefly describe some of the available WMS and discuss the current limitations of this methodology and the ways in which they can be overcome.
TL;DR: In this paper, specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open source Bioconductor software are described.
Abstract: This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.
TL;DR: The 10-year experience of the Alzforum is described, a unique example of a thriving scientific web community, and the SWAN (Semantic Web Applications in Neuromedicine) project is outlined, in which Alzforum curators are collaborating with informatics researchers to develop novel approaches that will enable communities to share richly contextualized information about scientific data, claims and hypotheses.
Abstract: Scientists drove the early development of the World Wide Web, primarily as a means for rapid communication, document sharing and data access. They have been far slower to adopt the web as a medium for building research communities. Yet, web-based communities hold great potential for accelerating the pace of scientific research. In this article, we will describe the 10-year experience of the Alzheimer Research Forum ('Alzforum'), a unique example of a thriving scientific web community, and explain the features that contributed to its success. We will then outline the SWAN (Semantic Web Applications in Neuromedicine) project, in which Alzforum curators are collaborating with informatics researchers to develop novel approaches that will enable communities to share richly contextualized information about scientific data, claims and hypotheses.
TL;DR: The purpose of this review is to focus on the three most important themes in genetic association studies using randomly selected patients and normal samples so that students and researchers alike who are new to this field may quickly grasp the key issues and command basic analysis methods.
Abstract: The purpose of this review is to focus on the three most important themes in genetic association studies using randomly selected patients (case, affected) and normal samples (control, unaffected), so that students and researchers alike who are new to this field may quickly grasp the key issues and command basic analysis methods. These three themes are: elementary categorical analysis; disease mutation as an unobserved entity; and the importance of homogeneity in genetic association analysis.
TL;DR: This review introduces the different strategies and computational methods that can be used in order to predict RNA genes and concludes with an outlook to future directions in algorithm development and data analyses.
Abstract: This review introduces the different strategies and computational methods that can be used in order to predict RNA genes. It discusses our current view of RNA genes as well as recent computational analyses of RNA genes and concludes with an outlook to future directions in algorithm development and data analyses.
TL;DR: This review briefly surveys available datasets in functional genomics, review methods for data integration and network alignment, and describes recent work on using network models to guide experimental validation, describing an important near-term milestone for systems biology.
Abstract: The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.
TL;DR: In reviewing provenance support, one of the important knowledge management issues in bioinformatics is reviewed and it is suggested that in Silico experimental protocols should themselves be a form of managing the knowledge of how to perform bioinformics analyses.
Abstract: This article offers a briefing in one of the knowledge management issues of in silico experimentation in bioinformatics. Recording of the provenance of an experiment-what was done; where, how and why, etc. is an important aspect of scientific best practice that should be extended to in silico experimentation. We will do this in the context of eScience which has been part of the move of bioinformatics towards an industrial setting. Despite the computational nature of bioinformatics, these analyses are scientific and thus necessitate their own versions of typical scientific rigour. Just as recording who, what, why, when, where and how of an experiment is central to the scientific process in laboratory science, so it should be in silico science. The generation and recording of these aspects, or provenance, of an experiment are necessary knowledge management goals if we are to introduce scientific rigour into routine bioinformatics. In Silico experimental protocols should themselves be a form of managing the knowledge of how to perform bioinformatics analyses. Several systems now exist that offer support for the generation and collection of provenance information about how a particular in silico experiment was run, what results were generated, how they were generated, etc. In reviewing provenance support, we will review one of the important knowledge management issues in bioinformatics.
TL;DR: The Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) was tasked with the creation of data standards and interchange formats to allow both the exchange and storage of such data irrespective of the hardware and software from which it was generated.
Abstract: The amount of data currently being generated by proteomics laboratories around the world is increasing exponentially, making it ever more critical that scientists are able to exchange, compare and retrieve datasets when re-evaluation of their original conclusions becomes important. Only a fraction of this data is published in the literature and important information is being lost every day as data formats become obsolete. The Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) was tasked with the creation of data standards and interchange formats to allow both the exchange and storage of such data irrespective of the hardware and software from which it was generated. This article will provide an update on the work of this group, the creation and implementation of these standards and the standards-compliant data repositories being established as result of their efforts.
TL;DR: A review of recently developed methods and their impact on core computational challenges currently facing proteomics is presented, including identification of proteins and peptides from tandem mass spectra as well as their quantitation.
Abstract: Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.
TL;DR: These issues with respect to chemistry, membrane microdomains and anomalous diffusion are reviewed and how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes are discussed.
Abstract: One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.
TL;DR: Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.
Abstract: Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g. for comparative 2D gel studies or LC-MS/MS analyses of complex protein mixtures, involve a number of steps: from experimental design, through wet and dry lab operations, to publication of data in repositories and finally to data annotation and maintenance. The presence of inaccuracies throughout the processing pipeline, however, results in data that can be untrustworthy, thus offsetting the benefits of high-throughput technology. While researchers and practitioners are generally aware of some of the information quality issues associated with public proteomics data, there are few accepted criteria and guidelines for dealing with them. In this article, we highlight factors that impact on the quality of experimental data and review current approaches to information quality management in proteomics. Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.
TL;DR: The basic features of content management systems are discussed along with commonly used open source software and some examples of their use in biomedical research are given.
Abstract: A common challenge for bioinformaticians, in either academic or industry laboratory environments, is providing informatic solutions via the Internet or through a web browser. Recently, the open source community began developing tools for building and maintaining web applications for many disciplines. These content management systems (CMS) provide many of the basic needs of an informatics group, whether in a small company, a group within a larger organisation or an academic laboratory. These tools aid in managing software development, website development, document development, course development, datasets, collaborations and customers. Since many of these tools are extensible, they can be developed to support other research-specific activities, such as handling large biomedical datasets or deploying bioanalytic tools. In this review of open source website management tools, the basic features of content management systems are discussed along with commonly used open source software. Additionally, some examples of their use in biomedical research are given.
TL;DR: With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.
Abstract: The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.
TL;DR: The latest developments in neuroscience information dissemination through the SenseLab suite of databases are presented, including the new features for each database, the evolution of SenseLab's unifying database architecture and instances of Senselab database interoperation with other neuroscience online resources.
Abstract: This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.
TL;DR: An overview of image-processing methods for Affymetrix GeneChips is presented, which improves the sensitivity of GeneC Chips and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.
Abstract: We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.