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align=\"center\"><a href=\"URL\"><img src=\"https://cloud.githubusercontent.com/assets/10756420/6043613/aa3f9592-ac8d-11e4-81d5-7ef2f1777510.png\" height=\"50%\" width=\"50%\" /></a></p>\r\n\r\nWe present EffectorPredictor (EP), a pipeline combining several pieces of software with the aim of returning a reliable identification of positively selected genes from pathogen genomes. After clustering by homology, EffectorPredictor performs state of the art positive selection analysis using codeml from the Paml package. Additional tools are run to provide an output integrating different sources of information, such as presence of Pfam domains, signal peptide for secretion and protein motifs.\r\n\r\n<p align=\"center\"><a href=\"URL\"><img src=\"https://cloud.githubusercontent.com/assets/10756420/5959695/9adc2762-a7d0-11e4-8b7a-51d92615337d.png\" height=\"90%\" width=\"90%\" /></a></p>\r\n\r\n\r\nA less stringent clustering is also run to provide a classification into homologous groups and to identify proteins similar to known effectors if these sequences are provided at the beginning of the analysis. Each homologous cluster is also used as an input to Meme to identify recurrent proteins motifs, as it is known that effectors are often characterized by those signatures."}}