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1.
Bioinformatics ; 31(9): 1499-501, 2015 May 01.
Article in English | MEDLINE | ID: mdl-25527096

ABSTRACT

MOTIVATION: Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in-the-algorithm' framework that provides common network inference functionality and user interface elements. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app. AVAILABILITY AND IMPLEMENTATION: Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store http://apps.cytoscape.org/apps/cynitoolbox CONTACT: benno.schwikowski@pasteur.fr.


Subject(s)
Gene Regulatory Networks , Software , Algorithms
2.
Science ; 335(6072): 1099-103, 2012 Mar 02.
Article in English | MEDLINE | ID: mdl-22383848

ABSTRACT

Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.


Subject(s)
Adaptation, Physiological , Bacillus subtilis/genetics , Bacillus subtilis/metabolism , Gene Regulatory Networks , Glucose/metabolism , Malates/metabolism , Metabolic Networks and Pathways/genetics , Algorithms , Bacterial Proteins/metabolism , Computer Simulation , Data Interpretation, Statistical , Gene Expression Regulation, Bacterial , Genome, Bacterial , Metabolome , Metabolomics , Models, Biological , Operon , Promoter Regions, Genetic , Transcription Factors/metabolism , Transcription, Genetic
3.
Science ; 335(6072): 1103-6, 2012 Mar 02.
Article in English | MEDLINE | ID: mdl-22383849

ABSTRACT

Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.


Subject(s)
Bacillus subtilis/genetics , Bacillus subtilis/physiology , Gene Expression Regulation, Bacterial , Promoter Regions, Genetic , Transcription, Genetic , Transcriptome , Adaptation, Physiological , Algorithms , Binding Sites , Gene Expression Profiling , Gene Regulatory Networks , Oligonucleotide Array Sequence Analysis , RNA, Antisense/genetics , RNA, Antisense/metabolism , RNA, Bacterial/genetics , RNA, Bacterial/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Regulon , Sigma Factor/metabolism , Terminator Regions, Genetic
4.
Proteomics ; 11(1): 22-32, 2011 Jan.
Article in English | MEDLINE | ID: mdl-21182191

ABSTRACT

One of the most common approaches for large-scale protein identification is LC, followed by MS. If more than a few proteins are to be identified, the additional fragmentation of individual peptides has so far been considered as indispensable, and thus, the associated costs, in terms of instrument time and infrastructure, as unavoidable. Here, we present evidence to the contrary. Using a combination of (i) highly accurate and precise mass measurements, (ii) modern retention time prediction, and (iii) a robust scoring algorithm, we were able to identify 257 proteins of Francisella tularensis from a single LC-MS experiment in a fragmentation-free approach (i.e. without experimental fragmentation spectra). This number amounts to 59% of the number of proteins identified in a standard fragmentation-based approach, when executed with the same false discovery rate. Independent evidence supports at least 27 of a set of 31 proteins that were identified only in the fragmentation-free approach. Our results suggest that additional developments in retention time prediction, measurement technology, and scoring algorithms may render fragmentation-free approaches an interesting complement or an alternative to fragmentation-based approaches.


Subject(s)
Chromatography, High Pressure Liquid , Computational Biology , Tandem Mass Spectrometry/methods , Francisella tularensis/metabolism , Peptides/metabolism , Proteins/metabolism
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