title: Bioinformatic analysis of expression data to identify effector candidates.
abstract: Pathogens produce effectors that manipulate the host to the benefit of the pathogen. These effectors are often secreted proteins that are upregulated during the early phases of infection. These properties can be used to identify candidate effectors from genomes and transcriptomes of pathogens. Here we describe commonly used bioinformatic approaches that (1) allow identification of genes encoding predicted secreted proteins within a genome and (2) allow the identification of genes encoding predicted secreted proteins that are upregulated at important stages of the life cycle. Other approaches for bioinformatic identification of effector candidates, including OrthoMCL analysis to identify expanded gene families, are also described.
authors.affiliation: Parasite Genomics, Wellcome Trust Sanger Institute, Genome Campus, Cambridge, CB10 1SA, UK, ar11@sanger.ac.uk.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Reid
authors.forename: John T
authors.initials: JT
authors.lastname: Jones
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015801:Helminth Proteins / Q000235:genetics* / Q000378:metabolism*; D009348:Nematoda / Q000235:genetics / Q000502:physiology; D010935:Plant Diseases / Q000469:parasitology; D017423:Sequence Analysis, RNA; D011198:Solanum tuberosum / Q000469:parasitology
chemical_list: D015801:Helminth Proteins