Dataset Information

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.

ABSTRACT: Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.

SUBMITTER: He J

PROVIDER: S-EPMC10457098 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.

He Jonathan J Liu Olivia O Guo Xuan X

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine 20221201

Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies pepti ...[more]

PMID: 37635836

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Project description:Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).

Project description:Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.

Dataset Information

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.

Publications

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.

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