The genome and proteome of the same cells are rarely measured together, which is especially consequential in cancer, where somatic mutations vary across clones and drive disease. We show that a single...

Precision proteogenomics analysis of 1,064 cancer patients across ten cancer types unveils the ways in which rare and common germline variants shape the cancer proteome; the findings highlight the con...

Organohalide-respiring bacteria (OHRB) are essential for breaking down harmful pollutants in the environment. This study investigates a newly discovered OHRB capable of degrading multiple contaminants...

Identifying novel peptides arising from alternative splicing, mutations, or non-canonical translations is a crucial yet challenging aspect of proteogenomics. We introduce PepCentric, a scalable computational platform and a web-based portal utilizing advanced 2-D fragment indexing for rapid peptide-centric searches across extensive mass spectrometry datasets. With robust false discovery rate control and optimized search performance, PepCentric offers an efficient tool for validating novel peptides and exploring proteomic variations. In a matter of seconds, users can search their novel peptides or proteins against 2.3 billion spectra collected from 66700 mass spectrometry runs, making it practical to rapidly validate proteogenomic hypotheses. ### Competing Interest Statement A.I.N., A.K., and F.Y. receive royalties from the University of Michigan for the sale of MSFragger software licenses to commercial entities. Other authors declare no competing interests.

The global SARS-CoV-2 pandemic emphasized the need for accurate pathogen diagnostics. While genomics is the gold standard, integrating mass spectrometry-based proteomics offers additional benefits. However, current proteomic and genomic reference databases are often biased toward specific taxa, such as pathogenic strains or model organisms, and proteomic databases are less comprehensive. These biases and gaps can lead to inaccurate identifications. To address these issues, we introduce MultiStageSearch, a multistep database search method that combines proteome and genome databases for taxonomic analysis. Initially, a generalist proteome database is used to infer potential species. Then, MultiStageSearch generates a specialized proteogenomic database for precise identification. This database is preprocessed to filter duplicates and cluster identical open reading frames to reduce genomic database biases. The workflow operates independently of strain-level NCBI taxonomy, enabling the identification of strains not represented in existing taxonomies. We benchmarked the workflow on viral and bacterial samples, demonstrating its superior performance in strain-level taxonomic inference compared to existing methods. MultiStageSearch offers a flexible and accurate approach for pathogen research and diagnostics, overcoming incomplete search spaces and biases inherent in reference databases.

Comparative proteogenomics reveal ecophysiological feats of Desulfobacteraceae for carbon and sulfur cycling in marine sediments.