Combining learning and constraints for genome-wide protein annotation.

ABSTRACT:

Background

The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.

Results

We present OCELOT, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and pro

SUBMITTER: Teso S 

PROVIDER: S-EPMC6580517 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature