Exploring the Uncharacterized Human Proteome Using neXtProt

Abstract

20,230 protein-coding genes have been predicted from the analysis of the human genome (neXtProt release 2018-01-17), and about 10% of them are still lacking functional annotation, either predicted by bioinformatics tools or captured from experimental reports. A systematic exploration of the available literature on uncharacterized human genes/proteins led to proposal of functional annotations for 113 proteins and to consolidation of a list of 1,862 uncharacterized human proteins. The advanced search functionality of neXtProt was used extensively in order to examine the landscape of the uncharacterized human proteome in terms of subcellular locations, protein-protein interactions, tissue expression, association with diseases, and 3D structure. Finally, a deep data mining in various publicly available resources allowed building functional hypotheses for 26 uncharacterized human proteins validated at protein level (uPE1). These hypotheses cover the fields of cilia biology, male reproduction, metabolism, nervous system, immunity, inflammation, RNA metabolism, and chromatin biology. They will require experimental validation before they can be considered for annotation. Despite technological progresses, the pace of human protein characterization studies is still slow. It could be accelerated by a better integration of existing knowledge resources and by initiating large collaborative projects involving specialists of different biology fields. We hope that our analysis will contribute to set up the ground for such collaborative approaches and will be exploited by the HUPO Human Proteome Project teams committed to characterize uPE1 proteins.

A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

“…The three data sources that provide evolutionary relationship data in RDF to represent HOGs are OrthoDB, MBGD and OMA. Although the RDF data models of MBGD and OMA both rely on the ORTH ontology (Fernández-Breis et al, 2016), they use different ORTH versions. However, SPARQL queries running over either of the two sources can be formulated in a very similar manner.…”

“…The main existing ontology to represent and structure the orthology information is the Orthology (ORTH) Ontology (Fernández-Breis, et al, 2016) that is recommended by the Quest for Orthologs Consortium (QfO). The second version of the ORTH ontology is further described at https://github.com/qfo/Ortholo-gyOntology) (de Farias et al, 2017).…”

“…To query and interoperate biological databases, Semantic Web Technologies are being increasingly adopted, in particular the use of the Resource Description Framework (RDF) and SPARQL protocol and RDF query language (SPARQL Query Language for RDF, n.d.). However, despite the progress they have enabled in several fields, particularly in the life sciences (Duek et al, 2018), (Iyappan et al, 2016) (Williams et al, 2012, there are still significant challenges that limit their use for the larger scientific community. In particular, analysing evolutionary relationship data in RDF poses the following challenges: 1) complex data models -for example, while storing data in a hierarchical structure (HOGs) results in significant performance benefits for common analyses, such as computing orthologs of a specific gene in a different model organism, the hierarchy also results in requiring advanced knowledge of the SPARQL language (in particular, recursivity) in order to benefit from the RDF representation of HOGs.…”

What will neXtProt help us achieve in 2020 and beyond?

“…Some functional annotations are based on the experimental characterization of the protein itself, while others are derived from characterization studies of similar proteins in human or model organisms. The neXtProt team is constantly scrutinizing the literature to find data to update these annotations [11]. Despite this effort, hundreds of proteins still lack any experimental or predicted functional annotation.…”

“…Based on deep data mining, the neXtProt team recently proposed functional hypotheses for 34 uncharacterized proteins [11,14], which are awaiting experimental confirmation. We are extending this strategy with a more extensive phylogenetic profiling approach.…”