The deluge of sequencing data needs curated association of structural and functional features to each sequence in the database. Inference of correct annotation is a major problem of sequence analysis. We highlight why it is difficult to solve the annotation task given the little amount of validated information in the database linking sequences, structure and experimental function in the most curated repository of protein sequences. We review state-of-the-art methods currently benchmarked in the Critical Assessment of protein Function Annotation algorithms (CAFA), an experiment designed to provide a large-scale assessment of computational methods dedicated to predicting protein function
Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio (2017). Protein Functional Annotation. Amsterdam : Elsevier [10.1016/B978-0-12-809633-8.12364-7].
Protein Functional Annotation
Pier Luigi Martelli;Giuseppe Profiti;Rita Casadio
2017
Abstract
The deluge of sequencing data needs curated association of structural and functional features to each sequence in the database. Inference of correct annotation is a major problem of sequence analysis. We highlight why it is difficult to solve the annotation task given the little amount of validated information in the database linking sequences, structure and experimental function in the most curated repository of protein sequences. We review state-of-the-art methods currently benchmarked in the Critical Assessment of protein Function Annotation algorithms (CAFA), an experiment designed to provide a large-scale assessment of computational methods dedicated to predicting protein functionI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
