False Discovery Rate Estimation Research Articles

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry (MS) based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo PSMs and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data.

An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.

Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. https://github.com/shawn-peng/xlms.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses.

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

On the use of tandem mass spectra acquired from samples of evolutionarily distant organisms to validate methods for false discovery rate estimation.

Estimating the false discovery rate (FDR) of peptide identifications is a key step in proteomics data analysis, and many methods have been proposed for this purpose. Recently, an entrapment-inspired protocol to validate methods for FDR estimation appeared in articles showcasing new spectral library search tools. That validation approach involves generating incorrect spectral matches by searching spectra from evolutionarily distant organisms (entrapment queries) against the original target search space. Although this approach may appear similar to the solutions using entrapment databases, it represents a distinct conceptual framework whose correctness has not been verified yet. In this viewpoint, we first discussed the background of the entrapment-based validation protocols and then conducted a few simple computational experiments to verify the assumptions behind them. The results reveal that entrapment databases may, in some implementations, be a reasonable choice for validation, while the assumptions underpinning validation protocols based on entrapment queries are likely to be violated in practice. This article also highlights the need for well-designed frameworks for validating FDR estimation methods in proteomics.

Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

Ion entropy and accurate entropy-based FDR estimation in metabolomics.

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Empirical Bayes single nucleotide variant-calling for next-generation sequencing data

One of the fundamental computational problems in cancer genomics is the identification of single nucleotide variants (SNVs) from DNA sequencing data. Many statistical models and software implementations for SNV calling have been developed in the literature, yet, they still disagree widely on real datasets. Based on an empirical Bayesian approach, we introduce a local false discovery rate (LFDR) estimator for germline SNV calling. Our approach learns model parameters without prior information, and simultaneously accounts for information across all sites in the genomic regions of interest. We also propose another LFDR-based algorithm that reliably prioritizes a given list of mutations called by any other variant-calling algorithm. We use a suite of gold-standard cell line data to compare our LFDR approach against a collection of widely used, state of the art programs. We find that our LFDR approach approximately matches or exceeds the performance of all of these programs, despite some very large differences among them. Furthermore, when prioritizing other algorithms’ calls by our LFDR score, we find that by manipulating the type I-type II tradeoff we can select subsets of variant calls with minimal loss of sensitivity but dramatic increases in precision.

AI-Assisted Processing Pipeline to Boost Protein Isoform Detection.

Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.

Biological factors and statistical limitations prevent detection of most noncanonical proteins by mass spectrometry.

Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry (MS) experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here, we leveraged recent advances in ribosome profiling and MS to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly expressed to be detected by shotgun MS at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for 4 noncanonical proteins in MS data, which were also supported by evolution and translation data. These results illustrate the power of MS to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly expressed proteins.

Discovery of Unexpected Phospholipid-Binding Proteins in Antiphospholipid Syndrome Patients

INTRODUCTION: The antiphospholipid syndrome (APS), initially described nearly 4 decades ago, remains enigmatic with unclear pathophysiology. In addition to the lack of a mechanistic diagnostic test, APS exhibits considerable phenotypic heterogeneity, including the occurrence of “non-consensus” clinical manifestations (i.e. other than vascular thrombosis and pregnancy complications). Despite the evidence that phospholipid-binding factors are highly likely to be central to APS pathogenesis, the full complement of phospholipid-binding proteins has yet to be systemically assayed. Proteomic profiling by label-free quantitation (LFQ) may identify patterns of protein binding to phospholipid that are central to APS pathogenesis and thereby elucidate a basis for the heterogeneous clinical presentations of APS. METHODS: C18 silica beads were coated with phospholipid by incubation with a suspension of 30% phosphatidylserine/70% phosphatidylcholine. Plasmas from 4 groups: 1) APS patients (n=11), 2) APL positive patients who lacked clinical manifestations (“APL”) (n=11), 3) non-APL patients with thrombosis (“nonAPL”)(n=11), and 4) normal healthy controls (“normal”) (n=6) were incubated with the phospholipid-coated beads, and eluates were processed by gel electrophoresis followed by tryptic digestion. The resulting peptides were analyzed by LC-MS/MS on a Thermo Orbitrap Fusion Lumos. Protein identification, false discovery rate (FDR) estimation, and LFQ were performed using Proteome Discoverer 2.2 (Thermo). LFQ values were normalized by overall peptide abundance, and relative quantitation was obtained. Groupwise comparisons were performed using PD 2.2 in order to identify differentially enriched proteins. RESULTS: A total of 1,210 proteins were identified, with 875 proteins identified at high confidence (q<0.01). Based on gel electrophoresis, all samples had comparable amounts of total protein, and normalization of LFQ data on the basis of total proteins was therefore performed. Differentially expressed proteins between groups were identified using both fold-change and -log p value cutoffs (Figure 1a). Primary analysis focused on identifying proteins that were consistently preferentially bound from the APS plasmas compared to the normal controls. Novel findings included the following proteins related to coagulation, immune response, and to lipid binding that bound preferentially from APS plasmas compared to normal plasmas (APS:control ratios shown in parentheses): coagulation factor VII (10.4), heparin cofactor 2 (9.6), CD14 (6.1), apolipoprotein L1 (6.0), and apolipoprotein L2 (5.5). Additional proteins that preferentially bound from APS plasmas included OAF (13.7), BRSK1 (13.2), MINPP1 (12.5), fetuin B (10.5), SHBG (8.9), ADAMTS-like protein 4 (8.5), SERPIN A4 (7.6), alpha-2-HS glycoprotein (6.7) and inter-alpha-trypsin inhibitor heavy chains (H3, H4 and H1; 8.5, 6.6, 6.5 respectively). Consistent with prior knowledge of APS, and validating this LFQ approach immunoglobulins and complement proteins were also among the preferentially bound proteins and included: Ig heavy chain 1-3 (15.6), complement C9 (13.3), complement C4-B, (7.1), complement component C8 beta chain (5.6), Ig kappa variable 1D-8 (5.6), and Ig lambda-like polypeptide 1 (3.9). Interestingly, a number of identified proteins showed intermediate abundance (between APS and control) in the APL positive patients without clinical manifestations (“APL”) and APL negative patients with thrombosis (“nonAPL”) (see figure 1B-D for examples), suggesting a potential dose response effect. CONCLUSIONS: 1) These results are a first demonstration of concept for the ability of LFQ proteomics to address the complexity of APS through analysis of phospholipid-bound proteins. 2) A number of phospholipid-binding proteins, many of which have not been previously implicated in APS pathogenesis, are preferentially present in APS patient plasma 3) Some plasmas from APL and non-APL patients also show increased binding of some of these proteins compared to the normal controls, suggesting that LFQ may identify markers for subgroups of patients within these 2 categories.

Local false discovery rate estimation with competition-based procedures for variable selection.

Multiple hypothesis testing has been widely applied to problems dealing with high-dimensional data, for example, the selection of important variables or features from a large number of candidates while controlling the error rate. The most prevailing measure of error rate used in multiple hypothesis testing is the false discovery rate (FDR). In recent years, the local false discovery rate (fdr) has drawn much attention, due to its advantage of accessing the confidence of individual hypotheses. However, most methods estimate fdr through -values or statistics with known null distributions, which are sometimes unavailable or unreliable. Adopting the innovative methodology of competition-based procedures, for example, the knockoff filter, this paper proposes a new approach, named TDfdr, to fdr estimation, which is free of -values or known null distributions. Extensive simulation studies demonstrate that TDfdr can accurately estimate the fdr with two competition-based procedures. We applied the TDfdr method to two real biomedical tasks. One is to identify significantly differentially expressed proteins related to the COVID-19 disease, and the other is to detect mutations in the genotypes of HIV-1 that are associated with drug resistance. Higher discovery power was observed compared to existing popular methods.

An improved permutation-based FDR estimator for heavy-tailed data

In false discovery rate (FDR) estimation, the permutation test is a widely used technique when the null distribution of the statistic is not easy to derive. We perceive that existing permutation-based FDR estimating methods perform well for normally-distributed data, but have shortcomings when handling heavy-tailed data. Heavy-tailed data exists widely in fields like finance, network, and medical science. We find that the number of variables Q included in the permutation subset significantly affects the FDR estimating result. This article defines a general mode of Q and proposes an improved parameter to determine the value of Q, according to the characteristics of the heavy-tailed data. Simulation results show that the proposed method performs well in handling heavy-tailed data and has smaller bias compared with existing methods. A microarray dataset is analyzed to illustrate the application of the new method.

Environment-wide association study to identify exposure pathways of bisphenol A in Korean children and adolescents: Korean National Environmental Health Survey (KoNEHS) 2018-2020

Bisphenol A (BPA) is an endocrine-disrupting chemical commonly used in manufacturing plastic products. Despite ongoing efforts and regulatory measures, BPA exposure among children persists. This study aimed to identify the modifiable factors associated with urinary BPA levels in Korean children and adolescents. We conducted an environment-wide association study (EWAS) using data from the Korean National Environmental Health Survey (KoNEHS) Cycle 4. This study included 578 preschoolers, 736 school-aged children, and 828 adolescents. A total of 117, 103, and 102 modifiable factors were selected from KoNEHS Cycle 4. Each modifiable factor associated with urinary BPA levels was tested using a multivariable linear regression model. Subsequently, multiple testing corrections were performed using false discovery rate (FDR) estimation. For the validation phase, we used the iteration of the least absolute shrinkage and selection operator (LASSO), a machine learning-based regression analysis. After the validation phase of the LASSO regression, two modifiable factors were identified as being significantly related to urinary BPA levels in preschoolers. Urinary cotinine levels and the use of slime or clay toys were positively associated with urinary BPA levels in preschoolers. However, no significant associations were observed between school-aged children and adolescents. Our results suggest novel exposure pathways to BPA in recent lifestyles and contribute to the development of effective prevention strategies. These modifiable factors provide valuable targets for interventions aimed at reducing BPA exposure in children. Further research is needed to explore additional modifiable factors and confirm our results in larger and more diverse populations.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity.

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexpected from large MS data. Molecular networking is a good tool for exploring, visualizing, and organizing MS/MS spectra, and moreover, it employs shifted peak match to calculate spectral similarity, making it capable of identifying adulteration that is not included in the library. The key of molecular networking is spectral similarity algorithms, and therefore, in this study, we compared the performance of four cutting-edge similarity algorithms, modified cosine similarity (shifted peak match), entropy similarity, and two deep-learning-based algorithms, MS2DeepScore and Spec2Vec, in building molecular networking for identification of adulteration that is not included in the library. We conducted an analysis of excluded-query-compound on all MS/MS spectra in test library and performed a large-scale false discovery rate estimation to investigate whether the spectral similarity calculated by each algorithm could represent the actual structural similarity well. The obtained results demonstrated Spec2Vec exhibited good performance in both detection capability and false discovery rate. Further comprehensive evaluation of the performance of Spec2Vec in the identification of adulteration that is not included in the library or is unexpected in different matrices and in application to real samples proved the approach studied here is a promising and powerful tool for adulterant inspection and improved the capability of analyzing large MS data.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Abstract 6577: CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy

Abstract Cancer immunotherapy has greatly improved the quality of life of cancer patients and it hinges on the discovery of novel cancer antigens that could be targeted to improve disease outcomes. The creation of databases such as IEDB, SysteMHC, TANTIGEN, caAtlas, HLA Ligand Atlas, Cancer Antigenic Peptide Database, SPENCER and IEAtlas support the immunopeptidomics community in understanding the landscape of antigen presentation. We have developed a pan-cancer, pan-HLA, and pan-tissue database containing immunopeptidomics data mapped to transcriptomic, genomic, immunological and biochemical data. The database was generated from 80 different publicly available immunopeptidomics mass spectrometry datasets collected between 2015-2022 (76 cancer and 4 normal datasets), covering 15 different types of cancers and 152 different HLA-I alleles. The peptides contained in our database were obtained by a combination of closed, open and de novo searches using an in-house developed computational pipeline. Following rigorous false discovery rate estimation at 1% and a second-round search to eliminate any false signals that may not have been detected in the previous round of FDR estimation, we obtained a list of 11.2 million peptide-HLA combinations comprising both coding and non-coding regions of the genome as well as bacterial peptides. These peptides have been mapped to chromosomal coordinates to facilitate adoption by the genomics community of this useful resource on antigen presentation. Pathway/biochemical analysis of each peptide was performed using the rWikiPathways package. Finally, mutations associated with each peptide were annotated using COSMIC and dbSNP resources. Our database includes a FAIR knowledge graph which contextualizes and enriches the data to enable clinicians to take effective therapeutic decisions on the appropriate form of treatment for cancer immunotherapy with the case study of clear cell renal cell carcinoma (ccRCC). We will continue to expand our database with new data over the next two years and expand the scope of its applications to facilitate uptake by the larger scientific community. Citation Format: Ashwin Adrian Kallor, Michał Waleron, Georges Bedran, Patrícia Eugénio, Catia Pesquita, Daniel Faria, Fabio Massimo Zanzotto, Christophe Battail, Ajitha Rajan, Javier Alfaro. CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6577.

Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics.

One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.