BACKGROUND: A number of different molecular interactions data download formats now exist, designed to allow access to these valuable data by diverse user groups. These formats include the PSI-XML and MITAB standard interchange formats developed by Molecular Interaction workgroup of the HUPO-PSI in addition to other, use-specific downloads produced by other resources. The onus is currently on the user to ensure that a piece of software is capable of read/writing all necessary versions of each format. This problem may increase, as data providers strive to meet ever more sophisticated user demands and data types. RESULTS: A collaboration between EMBL-EBI and the University of Cambridge has produced JAMI, a single library to unify standard molecular interaction data formats such as PSI-MI XML and PSI-MITAB. The JAMI free, open-source library enables the development of molecular interaction computational tools and pipelines without the need to produce different versions of software to read different versions of the data formats. CONCLUSION: Software and tools developed on top of the JAMI framework are able to integrate and support both PSI-MI XML and PSI-MITAB. The use of JAMI avoids the requirement to chain conversions between formats in order to reach a desired output format and prevents code and unit test duplication as the code becomes more modular. JAMI's model interfaces are abstracted from the underlying format, hiding the complexity and requirements of each data format from developers using JAMI as a library.

SUMMARY: Proteins frequently function as parts of complexes, assemblages of multiple proteins and other biomolecules, yet network visualizations usually only show proteins as parts of binary interactions. ComplexViewer visualizes interactions with more than two participants and thereby avoids the need to first expand these into multiple binary interactions. Furthermore, if binding regions between molecules are known then these can be displayed in the context of the larger complex. AVAILABILITY AND IMPLEMENTATION: freely available under Apache version 2 license; EMBL-EBI Complex Portal: http://www.ebi.ac.uk/complexportal; Source code: https://github.com/MICommunity/ComplexViewer; Package: https://www.npmjs.com/package/complexviewer; http://biojs.io/d/complexviewer. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js. CONTACT: [email protected] or [email protected].

BACKGROUND: Proteomic protein identification results need to be compared across laboratories and platforms, and thus a reliable method is needed to estimate false discovery rates. The target-decoy strategy is a platform-independent and thus a prime candidate for standardized reporting of data. In its current usage based on global population parameters, the method does not utilize individual peptide scores optimally. RESULTS: Here we show that proteomic analyses largely benefit from using separate treatment of peptides matching to proteins alone or in groups based on locally estimated false discovery rates. Our implementation reduces the number of false positives and simultaneously increases the number of proteins identified. Importantly, single peptide identifications achieve defined confidence and the sequence coverage of proteins is optimized. As a result, we improve the number of proteins identified in a human serum analysis by 58% without compromising identification confidence. CONCLUSION: We show that proteins can reliably be identified with a single peptide and the sequence coverage for multi-peptide proteins can be increased when using an improved estimation of false discovery rates.