Super paramagnetic clustering of protein sequences

Background: Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods. Results: Our work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Igor V. Tetko, Axel Facius, Andreas Ruepp, Hans-Werner Mewes
• Keywords: Algorithms, BMCBI 2005, Protein Families, Super Paramagnetic Clustering