Journal
NUCLEIC ACIDS RESEARCH
Volume 42, Issue D1, Pages D396-D400Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkt1079
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Funding
- DFG International Research Training Group 'Regulation and Evolution of Cellular Systems' [GRK 1563]
- Helmholtz Alliance for Systems Biology [NGFN: 01GR0451, FKZ: 0315494A, SysMBo]
- Federal Ministry of Education, Science, Research and Technology [NGFN: 01GR0451, FKZ: 0315494A, SysMBo]
- Institute for Bioinformatics and Systems Biology/MIPS, HMGU - German Research Center for Environmental Health
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Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz-muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%.
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