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1. SpecEncoder: deep metric learning for accurate peptide identification in proteomics

   https://doi.org/10.1093/bioinformatics/btae220  

   Liu, Kaiyuan; Tao, Chenghua; Ye, Yuzhen; Tang, Haixu (June 2024, Bioinformatics)

   Abstract MotivationTandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. ResultsWe evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%–2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%–15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%–12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder’s potential to enhance peptide identification for proteomic data analyses. Availability and ImplementationThe source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu. 

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2. Microbial mat activity and soil biogeochemistry across variable phosphorus availability in Taylor Valley, Antarctica

   https://doi.org/10.1017/S0954102025000094  

   Power, Sarah N; Osburn, Ernest D; Barrett, John E (May 2025, Antarctic Science)

   Abstract Primary production is fundamental to ecosystems, and in many extreme environments production is facilitated by microbial mats. Microbial mats are complex assemblages of photo- and heterotrophic microorganisms colonizing sediment and soil surfaces. These communities are the dominant producers of the McMurdo Dry Valleys, Antarctica, where they occupy lentic and lotic environments as well as intermittently wet soils. While the influence of microbial mats on stream nutrient dynamics and lake organic matter cycling is well documented, the influence of microbial mats on underlying soil is less well understood, particularly the effects of microbial mat nitrogen and carbon fixation. Taylor Valley soils occur across variable levels of inorganic phosphorus availability, with the Ross Sea drift containing four times that of the Taylor drifts, providing opportunities to examine how soil geochemistry influences microbial mats and the ecological functions they regulate. We found that inorganic phosphorus availability is positively correlated with microbial mat biomass, pigment concentration and nitrogen fixation potential. Additionally, our results demonstrate that dense microbial mats influence the ecological functioning of underlying soils by enriching organic carbon and total nitrogen stocks (two times higher). This work contributes to ongoing questions regarding the sources of energy fuelling soil food webs and the regional carbon balance in the McMurdo Dry Valleys. 

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3. Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data

   https://doi.org/10.1186/s40168-021-01035-8  

   Stamboulian, Moses; Li, Sujun; Ye, Yuzhen (December 2021, Microbiome)

   null (Ed.)

   Abstract Background A few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Methods Here, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification). Results We tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data. Conclusions The HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data. 

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4. APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

   https://doi.org/10.1093/gpbjnl/qzae042  

   Chen, Yiling Elaine; Ge, Xinzhou; Woyshner, Kyla; McDermott, MeiLu; Manousopoulou, Antigoni; Ficarro, Scott B; Marto, Jarrod A; Li, Kexin; Wang, Leo David; Li, Jingyi Jessica (April 2024, Genomics, Proteomics & Bioinformatics)

   Fu, Yan (Ed.)

   Abstract Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR. 

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5. Microbiomes and metabolomes of dominant coral reef primary producers illustrate a potential role for immunolipids in marine symbioses

   https://doi.org/10.1038/s42003-023-05230-1  

   Mannochio-Russo, Helena; Swift, Sean O. I.; Nakayama, Kirsten K.; Wall, Christopher B.; Gentry, Emily C.; Panitchpakdi, Morgan; Caraballo-Rodriguez, Andrés M.; Aron, Allegra T.; Petras, Daniel; Dorrestein, Kathleen; et al (August 2023, Communications Biology)

   Abstract The dominant benthic primary producers in coral reef ecosystems are complex holobionts with diverse microbiomes and metabolomes. In this study, we characterize the tissue metabolomes and microbiomes of corals, macroalgae, and crustose coralline algae via an intensive, replicated synoptic survey of a single coral reef system (Waimea Bay, Oʻahu, Hawaii) and use these results to define associations between microbial taxa and metabolites specific to different hosts. Our results quantify and constrain the degree of host specificity of tissue metabolomes and microbiomes at both phylum and genus level. Both microbiome and metabolomes were distinct between calcifiers (corals and CCA) and erect macroalgae. Moreover, our multi-omics investigations highlight common lipid-based immune response pathways across host organisms. In addition, we observed strong covariation among several specific microbial taxa and metabolite classes, suggesting new metabolic roles of symbiosis to further explore. 

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