Building a Gold-Standard Protein Sequence Dataset for Functional Annotation

[rababerladuseladim]

Abstract

Metaproteomics is the analysis of proteins in samples composed of multiple organisms. One major use case is the investigation of the functional composition of a sample. Multiple tools can connect identified sequences with functional information (e.g. Unipept, Prophane, MetaGOmics). Unfortunately, the performance of these tools is not easy to assess, due to a lack of data with known ground-truth at the functional level. The target benchmark dataset would consist of a diverse range of peptides/proteins with high-quality, experimentally validated functional annotations. The obstacles that need to be overcome for the creation of such a dataset are: (1) the further complicated protein inference issue in metaproteomics compared to single-organism proteomics (peptides can match to homologues in the same and multiple organisms) and (2) low annotation levels of proteins in the metaproteomic context (many proteins have no function - not even an assumed one - assigned to them). We plan to develop a concept on how the ideal gold standard dataset should be composed and generate it accordingly. Based on this dataset, a functional benchmark of the aforementioned tools can be initiated.

Work plan

• Compile sequence database of proteins with validated functions
• generate simulated peptide identification lists based on the database, closely resembling result characteristics in metaproteomics
• specify benchmarking criteria
• (Potentially) benchmark existing tools against generated data

Technical details

• datasets are derived from reference databases such as SwissProt
• tools for benchmarking:
  • Unipept
  • Prophane
  • MetaGOmics

Contact information

Henning Schiebenhoefer - Robert Koch-Institut (Germany) - [email protected]

[RalfG]

This hackathon project will be merged with #1 by @pverscha. See #1 for more info.