Homology-Based Annotation of Large Protein Datasets.
Advances in DNA sequencing technologies have led to an increasing amount of protein sequence data being generated. Only a small fraction of this protein sequence data will have experimental annotation associated with them. Here, we describe a protocol for in silico homology-based annotation of large protein datasets that makes extensive use of manually curated collections of protein families. We focus on annotations provided by the Pfam database and suggest ways to identify family outliers and family variations. This protocol may be useful to people who are new to protein data analysis, or who are unfamiliar with the current computational tools that are available.
Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de l'Ecole deMédecine, Paris, France. marco.punta@upmc.fr.
Marco
M
Punta
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Jaina
J
Mistry
D016000:Cluster Analysis; D003198:Computer Simulation; D030562:Databases, Protein; D020407:Internet; D058977:Molecular Sequence Annotation / Q000379:methods*; D005810:Multigene Family; D011506:Proteins / Q000235:genetics*; D017386:Sequence Homology, Amino Acid
D011506:Proteins
Homology; Profile-hidden Markov models; Protein annotation; Protein family databases; Sequence clustering