6,105 research outputs found
LAS: a software platform to support oncological data management
The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit
Discovering study-specific gene regulatory networks
This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets
An object-oriented framework to organize genomic data
Bioinformatics resources should provide simple and flexible support for genomics research. A huge amount of gene mapping data, micro-array expression data, expressed sequence tags (EST), BAC sequence data and genome sequence data are already, or will soon be available for a number of livestock species. These species will have different requirements compared to typical biomedical model organisms and will need an informatics framework to deal with the data. In term of exploring complex-intertwined genomic data, the way to organize them will be addressed in this study. Therefore, we investigated two issues in this study: one is an independent informatics framework including both back end and front end; another is how an informatics framework simplifies the user interface to explore data. We have developed a fundamental informatics framework that makes it easy to organize and manipulate the complex relations between genomic data, and allow for query results to be presented via a user friendly web interface. A genome object-oriented framework (GOOF) was proposed with object-oriented Java technology and is independent of any database system. This framework seamlessly links the database system and web presentation components. The data models of GOOF collect the data relationships in order to provide users with access to relations across different types of data, meaning that users avoid constructing queries within the interface layer. Moreover, the module-based interface provided by GOOF could allow different users to access data in different interfaces and ways. In another words, GOOF not only gives a whole solution to informatics infrastructure, but also simplifies the organization of data modeling and presentation. In order to be a fast development solution, GOOF provides an automatic code engine by using meta-programming facilities in Java, which could allow users to generate a large amount of routine program codes. Moreover, the pre-built data layer in GOOF connecting with Chado simplifies the process to manage genomic data in the Chado schema. In summary, we studied the way to model genomic data into an informatics framework, a one-stop approach, to organize the data and addressed how GOOF constructs a bioinformatics infrastructure for users to access genomic data
BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
Background: Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the
biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in
the use of models as well as the development of improved software systems and the availability of better, cheaper
computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model
repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in
these repositories should be extensively tested and encoded in community-supported and standardised formats. In
addition, the models and their components should be cross-referenced with other resources in order to allow their
unambiguous identification.
Description: BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a
freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative
models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by
BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled
vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various
formats. Reaction network diagrams generated from the models are also available in several formats. BioModels
Database also provides features such as online simulation and the extraction of components from large scale models
into smaller submodels. Finally, the system provides a range of web services that external software systems can use to
access up-to-date data from the database.
Conclusions: BioModels Database has become a recognised reference resource for systems biology. It is being used by
the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the
clustering of models based upon their annotations. Model deposition to the database today is advised by several
publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying
software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU
General Public License
Evaluation of Using Semi-Autonomy Features in Mobile Robotic Telepresence Systems
Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech. Plan Nacional de InvestigaciĂłn, proyecto DPI2011-25483
Compressing DNA sequence databases with coil
Background: Publicly available DNA sequence databases such as GenBank are large, and are
growing at an exponential rate. The sheer volume of data being dealt with presents serious storage
and data communications problems. Currently, sequence data is usually kept in large "flat files,"
which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which
rarely achieves good compression ratios. While much research has been done on compressing
individual DNA sequences, surprisingly little has focused on the compression of entire databases
of such sequences. In this study we introduce the sequence database compression software coil.
Results: We have designed and implemented a portable software package, coil, for compressing
and decompressing DNA sequence databases based on the idea of edit-tree coding. coil is geared
towards achieving high compression ratios at the expense of execution time and memory usage
during compression – the compression time represents a "one-off investment" whose cost is
quickly amortised if the resulting compressed file is transmitted many times. Decompression
requires little memory and is extremely fast. We demonstrate a 5% improvement in compression
ratio over state-of-the-art general-purpose compression tools for a large GenBank database file
containing Expressed Sequence Tag (EST) data. Finally, coil can efficiently encode incremental
additions to a sequence database.
Conclusion: coil presents a compelling alternative to conventional compression of flat files for the
storage and distribution of DNA sequence databases having a narrow distribution of sequence
lengths, such as EST data. Increasing compression levels for databases having a wide distribution of
sequence lengths is a direction for future work
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