Speeding Up Percolator

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Approximately 50% of all heart failure (HF) diagnoses can be classified as HF with preserved ejection fraction (HFpEF). HFpEF is more prevalent in females compared with males, but the underlying mechanisms are unknown. We previously showed that pressure overload (PO) in male felines induces a cardiopulmonary phenotype with essential features of human HFpEF. The goal of this study was to determine if slow progressive PO induces distinct cardiopulmonary phenotypes in females and males in the absence of other pathological stressors. Female and male felines underwent aortic constriction (banding) or sham surgery after baseline echocardiography, pulmonary function testing, and blood sampling. These assessments were repeated at 2 and 4 mo postsurgery to document the effects of slow progressive pressure overload. At 4 mo, invasive hemodynamic studies were also performed. Left ventricle (LV) tissue was collected for histology, myofibril mechanics, extracellular matrix (ECM) mass spectrometry, and single-nucleus RNA sequencing (snRNAseq). The induced pressure overload (PO) was not different between sexes. PO also induced comparable changes in LV wall thickness and myocyte cross-sectional area in both sexes. Both sexes had preserved ejection fraction, but males had a slightly more robust phenotype in hemodynamic and pulmonary parameters. There was no difference in LV fibrosis and ECM composition between banded male and female animals. LV snRNAseq revealed changes in gene programs of individual cell types unique to males and females after PO. Based on these results, both sexes develop cardiopulmonary dysfunction but the phenotype is somewhat less advanced in females.NEW & NOTEWORTHY We performed a comprehensive assessment to evaluate the effects of slow progressive pressure overload on cardiopulmonary function in a large animal model of heart failure with preserved ejection fraction (HFpEF) in males and females. Functional and structural assessments were performed at the organ, tissue, cellular, protein, and transcriptional levels. This is the first study to compare snRNAseq and ECM mass spectrometry of HFpEF myocardium from males and females. The results broaden our understanding of the pathophysiological response of both sexes to pressure overload. Both sexes developed a robust cardiopulmonary phenotype, but the phenotype was equal or a bit less robust in females.

TIDD: tool-independent and data-dependent machine learning for peptide identification

Background

In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.

Results

We propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience.

Conclusions

TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04640-y.

Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus

MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.

Performing Selection on a Monotonic Function in Lieu of Sorting Using Layer-Ordered Heaps

Nonparametric statistical tests are an integral part of scientific experiments in a diverse range of fields. When performing such tests, it is standard to sort values; however, this requires Ω(n log(n)) time to sort n values. Thus given enough data, sorting becomes the computational bottleneck, even with very optimized implementations such as the C++ standard library routine, std::sort. Frequently, a nonparametric statistical test is only used to partition values above and below a threshold in the sorted ordering, where the threshold corresponds to a significant statistical result. Linear-time selection and partitioning algorithms cannot be directly used because the selection and partitioning are performed on the transformed statistical significance values rather than on the sorted statistics. Usually, those transformed statistical significance values (e.g., the p value when investigating the family-wise error rate and q values when investigating the false discovery rate (FDR)) can only be computed at a threshold. Because this threshold is unknown, this leads to sorting the data. Layer-ordered heaps, which can be constructed in O(n), only partially sort values and thus can be used to get around the slow runtime required to fully sort. Here we introduce a layer-ordering-based method for selection and partitioning on the transformed values (e.g., p values or q values). We demonstrate the use of this method to partition peptides using an FDR threshold. This approach is applied to speed up Percolator, a postprocessing algorithm used in mass-spectrometry-based proteomics to evaluate the quality of peptide-spectrum matches (PSMs), by >70% on data sets with 100 million PSMs.