Title: Neighborhood-Based Label Propagation in Large Protein Graphs,
Abstract: Understanding protein function is one of the keys to understanding life at
the molecular level. It is also important in several scenarios including human
disease and drug discovery. In this age of rapid and affordable biological
sequencing, the number of sequences accumulating in databases is rising with an
increasing rate. This presents many challenges for biologists and computer
scientists alike. In order to make sense of this huge quantity of data, these
sequences should be annotated with functional properties. UniProtKB consists of
two components: i) the UniProtKB/Swiss-Prot database containing protein
sequences with reliable information manually reviewed by expert bio-curators
and ii) the UniProtKB/TrEMBL database that is used for storing and processing
the unknown sequences. Hence, for all proteins we have available the sequence
along with few more information such as the taxon and some structural domains.
Pairwise similarity can be defined and computed on proteins based on such
attributes. Other important attributes, while present for proteins in
Swiss-Prot, are often missing for proteins in TrEMBL, such as their function
and cellular localization. The enormous number of protein sequences now in
TrEMBL calls for rapid procedures to annotate them automatically. In this work,
we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation
approach for large-scale annotation of proteins. To do this, the functional
annotations of reviewed proteins are used to predict those of non-reviewed
proteins using label propagation on a graph representation of the protein
database. DistNBLP is built on top of the "akka" toolkit for building resilient
distributed message-driven applications.