MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Closed reduction and surgical fixation of elbow joint luxation in an adult mare and in a 3‐month‐old foal using knotless bone anchors and FiberTape

This report describes the successful surgical stabilisation of elbow joint luxation using knotless bone anchors and FiberTape to repair the medial collateral ligament in an 11-year-old mare (Case 1) and both medial and lateral collateral ligaments in a 3-month-old foal (Case 2). Radiography (both cases) and computed tomography (Case 2) were used to diagnose subluxation (Case 1) and complete luxation (Case 2) with suspected rupture of both collateral ligaments. Case 1 was supported in a rescue sling for 7 weeks postoperatively to prevent recumbency. Recovery was good, and the mare was sound at the walk at the time of discharge 8 weeks after surgery and 1 year later at the follow-up visit. Case 2 was discharged 3 weeks after surgery and was sound at the trot when re-evaluated 2 and 6 months postoperatively. Although elbow luxation accompanied by torn collateral ligaments is rare in horses, successful outcomes have been described. In the present case report, joint stabilisation and a successful outcome were achieved, for the first time, with closed reduction and an open approach for placement of knotless bone anchors and FiberTape. Limitations of our study included inability to directly assess the integrity of the implants during healing and relying mainly on clinical signs to assess healing

Modello regionale di presa in carico del bambino sovrappeso e obeso

n.a