10,856 research outputs found
eXamine: a Cytoscape app for exploring annotated modules in networks
Background. Biological networks have growing importance for the
interpretation of high-throughput "omics" data. Statistical and combinatorial
methods allow to obtain mechanistic insights through the extraction of smaller
subnetwork modules. Further enrichment analyses provide set-based annotations
of these modules.
Results. We present eXamine, a set-oriented visual analysis approach for
annotated modules that displays set membership as contours on top of a
node-link layout. Our approach extends upon Self Organizing Maps to
simultaneously lay out nodes, links, and set contours.
Conclusions. We implemented eXamine as a freely available Cytoscape app.
Using eXamine we study a module that is activated by the virally-encoded
G-protein coupled receptor US28 and formulate a novel hypothesis about its
functioning
metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella
The metabolic SearcH And Reconstruction Kit
(metaSHARK) is a new fully automated software package
for the detection of enzyme-encoding genes
within unannotated genome data and their visualization
in the context of the surrounding metabolic network.
The gene detection package (SHARKhunt) runs
on a Linux systemand requires only a set of raw DNA
sequences (genomic, expressed sequence tag and/
or genome survey sequence) as input. Its output
may be uploaded to our web-based visualization
tool (SHARKview) for exploring and comparing data
from different organisms. We first demonstrate the
utility of the software by comparing its results for
the raw Plasmodium falciparum genome with the
manual annotations available at the PlasmoDB and
PlasmoCyc websites. We then apply SHARKhunt to
the unannotated genome sequences of the coccidian
parasite Eimeria tenella and observe that, at an
E-value cut-off of 10(-20), our software makes 142
additional assertions of enzymatic function compared
with a recent annotation package working
with translated open reading frame sequences. The
ability of the software to cope with low levels of
sequence coverage is investigated by analyzing
assemblies of the E.tenella genome at estimated
coverages from 0.5x to 7.5x. Lastly, as an example
of how metaSHARK can be used to evaluate the
genomic evidence for specific metabolic pathways,
we present a study of coenzyme A biosynthesis in
P.falciparum and E.tenella
Reconstruction of an in silico metabolic model of _Arabidopsis thaliana_ through database integration
The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.

Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant _Arabidopsis thaliana_, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a _core metabolic model_ containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an _intermediate metabolic model_ attempts to fill gaps and provides better continuity. Third, a _complete metabolic model_ contains the full set of known metabolic reactions and compounds in _Arabidopsis thaliana_.

We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry
Updates in metabolomics tools and resources: 2014-2015
Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table
NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps
Molecular biology knowledge can be systematically represented in a
computer-readable form as a comprehensive map of molecular interactions. There
exist a number of maps of molecular interactions containing detailed
description of various cell mechanisms. It is difficult to explore these large
maps, to comment their content and to maintain them. Though there exist several
tools addressing these problems individually, the scientific community still
lacks an environment that combines these three capabilities together. NaviCell
is a web-based environment for exploiting large maps of molecular interactions,
created in CellDesigner, allowing their easy exploration, curation and
maintenance. NaviCell combines three features: (1) efficient map browsing based
on Google Maps engine; (2) semantic zooming for viewing different levels of
details or of abstraction of the map and (3) integrated web-based blog for
collecting the community feedback. NaviCell can be easily used by experts in
the field of molecular biology for studying molecular entities of their
interest in the context of signaling pathways and cross-talks between pathways
within a global signaling network. NaviCell allows both exploration of detailed
molecular mechanisms represented on the map and a more abstract view of the map
up to a top-level modular representation. NaviCell facilitates curation,
maintenance and updating the comprehensive maps of molecular interactions in an
interactive fashion due to an imbedded blogging system. NaviCell provides an
easy way to explore large-scale maps of molecular interactions, thanks to the
Google Maps and WordPress interfaces, already familiar to many users. Semantic
zooming used for navigating geographical maps is adopted for molecular maps in
NaviCell, making any level of visualization meaningful to the user. In
addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte
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