Mass Spectrometry Data Analysis Research Articles

ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics.

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and Major histocompatibility complex (MHC) -peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical postprocessing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available online at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

Review and Practical Guide for Getting Started With Single-Cell Proteomics.

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

Talaketides A−G, linear polyketides with prostate cancer cytotoxic activity from the mangrove sediment-derived fungus Talaromyces sp. SCSIO 41027

Seven novel linear polyketides, talaketides A−G (1−7), were isolated from the rice media cultures of the mangrove sediment-derived fungus Talaromyces sp. SCSIO 41027. Among these, talaketides A−E (1−5) represented unprecedented unsaturated linear polyketides with an epoxy ring structure. The structures, including absolute configurations of these compounds, were elucidated through detailed analyses of nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HR-MS) data, as well as electronic custom distributors (ECD) calculations. In the cytotoxicity screening against prostate cancer cell lines, talaketide E (5) demonstrated a dose-dependent inhibitory effect on prostate cancer PC-3 cell lines, with an IC50 value of 14.44 μmol·L−1 . Moreover, compound 5 significantly inhibited the cloning formation of PC-3 cell lines and arrested the cell cycle in S-phase, ultimately inducing apoptosis. These findings indicate that compound 5 may serve as a promising lead compound for the development of a potential treatment for prostate cancer.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Identification of novel microcystins in algal extracts by a liquid chromatography–high-resolution mass spectrometry data analysis pipeline

BackgroundMicrocystins are an emergent public health problem. These toxins are secondary metabolites of harmful cyanobacterial blooms, with blooms becoming more prevalent with eutrophication of water. Exposure to microcystins can result in sickness, liver damage, and even death. Over 300 microcystins have been identified to date, with differences in toxicity based on the specific amino acid composition. Because of this diversity in microcystins, as well as the likelihood of detecting as yet undiscovered microcystins, it is vital to establish a methodological workflow to identify any microcystin in a complex sample, regardless of the availability of a reference standard. Additionally, ascribing varying levels of confidence to these identifications is critical to effectively communicate discoveries. MethodsA liquid-chromatography–high-resolution mass spectrometry method was utilized to identify microcystins present in cyanobacterial extracts from a strain of Microcystis aeruginosa and an Aphanizomenon sp. First, microcystin congeners with available standards were identified in the cyanobacterial extract. These known-unknown microcystins were considered to have the highest confidence identifications due to availability of accurate masses, retention times, and library spectra for comparison. Utilizing the spectra of these microcystins, relatively high-abundance diagnostic product-ions were identified and employed to screen the data for additional candidate microcystins. Microcystins without a standard that had an exact mass matching a microcystin published in CyanoMetDB were considered semi-known-unknown microcystins. The remaining microcystins were considered unknown-unknown microcystins. The identities of the microcystins determined herein were additionally supported by product-ion analysis, thiol reactivity, esterification reactions, neutral loss analysis, and literature contextualization. ResultsIn total, utilizing the systematic workflow presented herein, 23 microcystins were identified in the M. aeruginosa culture, including two not published previously: [d-Asp3]MC-LCit and the incompletely identified MC-L(C7H11NO3).

Investigating the efficacy of naphthalene-thiazole hybrid hydrazones as α-glucosidase inhibitors

In this study, a series of hydrazone derivatives containing naphthalene and thiazole hybrids were synthesized through the condensation of (E)-4-methyl-2-(2-(naphthalen-1-ylmethylene)hydrazineyl) thiazole-5-carbohydrazide with various carbonyl compounds, including aldehydes and ketones, whether carbocyclic aromatic or heterocyclic, using a grinding method with drops of acetic acid. The elucidation of the structures of the synthesized hydrazone derivatives was achieved through the analysis of NMR, IR, and mass spectrometry data. All the newly prepared compounds were assessed for their in vitro α-glucosidase inhibition profiles and the results revealed a diverse range of inhibitory potentials, ranging from 9.3 ± 1.0 to 70.5 ± 1.8 μM, in contrast to the reference acarbose (with an IC50 value of 45.2 ± 1.3 μM). In an attempt to explain the observed inhibitions of synthesized naphthalene containing thiazole compounds, molecular docking was performed to determine the binding modes between the synthesized compounds from one side and the active residues of α-glucosidase from the other side. Compounds 4d, 4e, 4c, and 8 demonstrated the strongest binding affinities, with binding energies of -10.5, -9.8, -9.6, and -9.5 kcal/mol, respectively.

Calycosin-7-O-β-D-Glucoside Ameliorates Palmitate-Induced Lipid Accumulation in HT22 Cells.

The pathogenesis of Alzheimer's disease (AD) is complex. Recent research suggests that AD patients have early disorders in brain cholesterol metabolism. Cholesterol and its derivatives accumulate in neurons, leading to p-Tau overproduction and synaptic dysfunction, initiating AD progression. Calycosin-7-O-β-D-glucoside (CG), a distinctive constituent of Astragali Radix, holds a representative position. Many clinical trials have demonstrated that CG can attenuate cerebral ischemia/reperfusion injury and preserve the structural integrity of the blood-brain barrier. However, whether CG alleviates tau-mediated neurodegeneration by increasing cholesterol efflux after lipid accumulation remains unexplored. Ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) and multivariate data analysis were employed to investigate metabolic changes in HT22 cells induced by sodium palmitate following 24 hours of CG treatment. The potential therapeutic mechanisms of CG on AD were further examined through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Metabolomic analysis characterized 24 potential biomarkers, revealing that CG could ameliorate cholesterol metabolic pathways. The results of cell experiments revealed that CG can increase the expression of enzyme cholesterol 24-hydroxylase (CYP46A1) (p < 0.05) and the level of 24 hydroxycholesterol (24-OHC) (p < 0.05), reduce the expression of p-Tau (Thr231)/Tau (p < 0.01), inhibit the formation of lipid droplets. CG may inhibit the accumulation of cholesterol and its derivatives in neurons by affecting the CYP46A1-CE-Tau axis, offering a potential therapeutic strategy for AD.

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy.

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Fungal Coculture of Herpotrichia sp. and Trametes versicolor Induces Production of Diverse Metabolites with Anti-Parkinson's Neuroprotective Activity.

Co-cultivation of isopod-associated fungi Herpotrichia sp. SF09 and Trametes versicolor SF09A led to the reciprocal induction of thirteen new compounds (1-7 and 9-13) with diverse architectures. Importantly, compounds 1 and 2 are rare fungal sesquiterpene-saccharide hybrids incorporating a xylopyranose moiety, compound (±)-3 represents the first example of a natural linear sesquiterpene racemate, and compound 7 is a rare α-pyrone derivative with a xylopyranose motif. Their structures were elucidated by analysis of NMR and mass spectrometry data, and their absolute configurations were determined by Mosher's method, microscale derivatization, and single-crystal X-ray diffraction, as well as ECD calculations. All the isolated compounds ameliorated MPP+-induced oxidative damage in PC12 cells in a dose-dependent fashion. Among them, compounds 5 and 15 showed significant protective action against neuronal injury by MPP+ at 5 μM. Meanwhile, transcriptome sequencing was performed to evaluate the molecular mechanism of the neuroprotective activity for compound 5. Results indicated that compound 5 might mitigate MPP+-induced neuronal injury through the regulation of multiple signaling pathways, including the PI3K-Akt and MAPK pathways. Our findings suggested that compound 5 could be a promising neuroprotective agent for the treatment of Parkinson's disease.

An Expedited Qualitative Profiling of Free Amino Acids in Plant Tissues Using Liquid Chromatography-Mass Spectrometry (LC-MS) in Conjunction With MS-DIAL.

The estimation of relative levels of amino acids is crucial for understanding various biological processes in plants, including photosynthesis, stress tolerance, and the uptake and translocation of nutrients. A wide range of liquid chromatography (LC; HPLC/UHPLC)-based methods is available for measuring the quantity of amino acids in plants. Additionally, the coupling of LC with mass spectrometry (MS) significantly enhanced the robustness of existing chromatographic methods used for amino acid quantification. However, accurate annotation and integration of mass peaks can be challenging for plant biologists with limited experience in analyzing MS data, especially in studies involving large datasets with multiple treatments and/or replicates. Further, there are instances when the experiment demands an overall view of the amino acids profile rather than focusing on absolute quantification. The present protocol provides a detailed LC-MS method for obtaining a qualitative amino acids profile using MS-DIAL, a versatile and user-friendly program for processing MS data. Free amino acids were extracted from the leaves of control and Tomato leaf curl Palampur virus (ToLCPalV)-infected Nicotiana benthamiana plants. Extracted amino acids were derivatized and separated using UHPLC-QTOF, with each amino acid subsequently identified by aligning mass data with a custom text library created in MS-DIAL. Further, MS-DIAL was employed for internal standard-based normalization to obtain a qualitative profile of 15 amino acids in control and virus-infected plants. The outlined method aims to simplify the processing of MS data to quickly assess any modulation in amino acid levels in plants with a higher degree of confidence.

MetaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score=0.67-0.90) in genus diversity and showed a high correlation (rSpearman=0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

PathMHC: a workflow to selectively target pathogen-derived MHC peptides in discovery immunopeptidomics experiments for vaccine target identification.

Vaccine-elicited T cell responses can contribute to immune protection against emerging infectious disease risks such as antimicrobials-resistant (AMR) microbial pathogens and viruses with pandemic potential, but rapidly identifying appropriate targets for T cell priming vaccines remains challenging. Mass spectrometry (MS) analysis of peptides presented on major histocompatibility complexes (MHCs) can identify potential targets for protective T cell responses in a proteome-wide manner. However, pathogen-derived peptides are outnumbered by self peptides in the MHC repertoire and may be missed in untargeted MS analyses. Here we present a novel approach, termed PathMHC, that uses computational analysis of untargeted MS data followed by targeted MS to discover novel pathogen-derived MHC peptides more efficiently than untargeted methods alone. We applied this workflow to identify MHC peptides derived from multiple microbes, including potential vaccine targets presented on MHC-I by human dendritic cells infected with Mycobacterium tuberculosis . PathMHC will facilitate antigen discovery campaigns for vaccine development.

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

SynONIM: A Comprehensive Database of Synthetic Oligonucleotide Modifications and Impurities to Aid in Their Characterization by Mass Spectrometry.

Given the resurgence of oligonucleotides in the biotherapeutic space, there is a profound focus on their characterization by mass spectrometry. These therapeutic moieties commonly employ synthetic modifications to aid in increasing efficacy and stability; however, these modifications can also increase the complexity of mass spectrometry data analysis. Additionally, various stress conditions can affect both the observed level and type of impurities stemming from the variety of utilized modifications. Within the oligonucleotide analytical development community, a clear desire exists for a unified database of synthetic oligonucleotide modifications and impurities where information regarding structure, mass, and shorthand nomenclature can be contained. To address this, the authors have prepared an online database and webtool of synthetic oligonucleotide impurities and modifications, SynONIM, to centrally locate information key to the mass spectrometry community. SynONIM can be queried by elemental composition lost or gained, mass shift, shorthand notation, nucleotide location, and species origin.

In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis

Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary β-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection.

Analysis of Mucin-Domain Glycoproteins Using Mass Spectrometry.

Mucin-domain glycoproteins are characterized by their high density of glycosylated serine and threonine residues, which complicates their analysis by mass spectrometry. The dense glycosylation renders the protein backbone inaccessible to workhorse proteases like trypsin, the vast heterogeneity of glycosylation often results in ion suppression from unmodified peptides, and search algorithms struggle to confidently analyze and site-localize O-glycosites. We have made a number of advances to address these challenges, rendering mucinomics possible for the first time. Here, we summarize these contributions and provide a detailed protocol for mass spectrometric analysis of mucin-domain glycoproteins. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Enrichment of mucin-domain glycoproteins Basic Protocol 2: Enzymatic digestion of mucin-domain glycoprotein(s) Basic Protocol 3: Mass spectrometry data collection for O-glycopeptides Basic Protocol 4: Mass spectrometry data analysis of O-glycopeptides.

A learned score function improves the power of mass spectrometry database search.

One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.

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

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.