Identifying protein complexes directly from high-throughput TAP data with Markov random fields

<p>Abstract</p> <p>Background</p> <p>Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.</p> <p>Results</p> <p>We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.</p> <p>Conclusion</p> <p>We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.</p

Multi-risk assessment and management—a comparative study of the current state of affairs in chile and ecuador

In Chile and Ecuador, multiple hazards and dynamic processes in vulnerability pose a high risk. Spatial planning and emergency management can contribute to disaster risk management but they follow different goals. However, global goals, such as from UN-ISDR (United Nat

Antennal asymmetry is not associated with social behaviour in Australian Hymenoptera

Lateralisation of biological form and function are well known for vertebrates and are being increasingly documented among invertebrates in recent years. Behavioural lateralisation in insects, together with asymmetrical distributions of antennal sensilla, has been linked to the communication challenges faced by social, but not solitary, insects. Recent evidence on patterns of asymmetry in insects outside of the Hymenoptera suggests that this explanation for antennal sensilla asymmetry may not be phylogenetically constrained. We explore this possibility by examining the distribution of antennal sensilla in three species of ants (Formicidae), the meat ant Iridomyrmex purpureus (Dolichoderinae), the green tree ant Oecophylla smaragdina (Formicinae) and the shield ant Meranoplus sp. (Myrmicinae) in which colony organisation is eusocial, and two species of nomiine bees, Mellitidia tomentifera and Reepenia bituberculata (Halictidae: Nomiinae), where colony organisation is not eusocial. Our results demonstrate that while there are differences in the left–right asymmetry of antennal sensilla basiconica in workers of the formicine ant I. purpureus, there is no consistent sensilla asymmetry across the five species. We find a negative correlation between antennal sensilla density and body size in R. bituberculata, which was not apparent in the other species. Our results contradict the suggestion that asymmetrical distribution of antennal sensilla is associated with the evolution of eusocial behaviour