neXtProt: a knowledge platform for human proteins.

neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB), it aims to help researchers answer questions relevant to human proteins. To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected and filtered high-throughput data pertinent to human proteins. This article presents an overview of the database and the data integration process. We also lay out the key future directions of neXtProt that we consider the necessary steps to make neXtProt the one-stop-shop for all research projects focusing on human proteins.

neXtProt: organizing protein knowledge in the context of human proteome projects.

About 5000 (25%) of the ~20400 human protein-coding genes currently lack any experimental evidence at the protein level. For many others, there is only little information relative to their abundance, distribution, subcellular localization, interactions, or cellular functions. The aim of the HUPO Human Proteome Project (HPP, www.thehpp.org ) is to collect this information for every human protein. HPP is based on three major pillars: mass spectrometry (MS), antibody/affinity capture reagents (Ab), and bioinformatics-driven knowledge base (KB). To meet this objective, the Chromosome-Centric Human Proteome Project (C-HPP) proposes to build this catalog chromosome-by-chromosome ( www.c-hpp.org ) by focusing primarily on proteins that currently lack MS evidence or Ab detection. These are termed "missing proteins" by the HPP consortium. The lack of observation of a protein can be due to various factors including incorrect and incomplete gene annotation, low or restricted expression, or instability. neXtProt ( www.nextprot.org ) is a new web-based knowledge platform specific for human proteins that aims to complement UniProtKB/Swiss-Prot ( www.uniprot.org ) with detailed information obtained from carefully selected high-throughput experiments on genomic variation, post-translational modifications, as well as protein expression in tissues and cells. This article describes how neXtProt contributes to prioritize C-HPP efforts and integrates C-HPP results with other research efforts to create a complete human proteome catalog.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

BACKGROUND: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. OBJECTIVE: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research. METHODS: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. RESULTS: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. CONCLUSION: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics.

In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.