Database-independent de novo metaproteomics of complex microbial communities
peer-reviewed · Cell Systems · 2021
| Date | 2021-05-01 |
| Type | peer-reviewed |
| Venue | Cell Systems |
| Publisher | Elsevier BV |
| Contribution | downstream-application |
| DOI | 10.1016/j.cels.2021.04.003 |
| Citations (OpenAlex) | 74 |
| Venue 2-year citedness | 6.21 |
Preprint version: Quantitative profiling of microbial communities by de novo metaproteomics (2020-08-17, bioRxiv)
Abstract
Metaproteomics has emerged as one of the most promising approaches for determining the composition and metabolic functions of complete microbial communities. Conventional metaproteomics approaches rely on the construction of protein sequence databases and efficient peptide-spectrum-matching algorithms, an approach that is intrinsically biased towards the content of the constructed sequence database. Here, we introduce a highly efficient, database-independent de novo metaproteomics approach and systematically evaluate its quantitative performance using synthetic and natural microbial communities comprising dozens of taxonomic families. Our work demonstrates that the de novo sequencing approach can vastly expand many metaproteomics applications by enabling rapid quantitative profiling and by capturing unsequenced community members that otherwise remain inaccessible for further interpretation. Kleikamp et al., describe a novel de novo metaproteomics pipeline (NovoBridge) that enables rapid community profiling without the need for constructing protein sequence databases.
Methods and tools
- NovoBridge: A database-independent metaproteomics pipeline that maps de novo peptide sequences to taxonomy and function, giving quantitative community profiles without a sample-specific database.
Methods it uses
- PEAKS: Commercial DP-based de novo