bioRxiv Science⌕ Search

bioRxiv · 10.1101/831776

Tailor: non-parametric and rapid score calibration method for database search-based peptide identification in shotgun proteomics

Abstract

Peptide-spectrum-match (PSM) scores used in database searching are calibrated to spectrum- or spectrum-peptide-specific null distributions. Some calibration methods rely on specific assumptions and use analytical models (e.g. binomial distributions), whereas other methods utilize exact empirical null distributions. The former may be inaccurate because of unjustified assumptions, while the latter are accurate, albeit computationally exhaustive. Here, we introduce a novel, non-parametric, heuristic PSM score calibration method, called Tailor, which calibrates PSM scores by dividing it with the top 100-quantile of the empirical, spectrum-specific null distributions (i.e. the score with an associated p-value of 0.01 at the tail, hence the name) observed during database searching. Tailor does not require any optimization steps or long calculations; it does not rely on any assumptions on the form of the score distribution, it works with any score functions with high- and low-resolution information. In our benchmark, we re-calibrated the match scores of XCorr from Crux, HyperScore scores from X!Tandem, and the p-values from OMSSA with Tailor method, and obtained more spectrum annotation than with raw scores at any false discovery rate level. Moreover, Tailor provided slightly more annotations than E-values of X!Tandem and OMSSA and approached the performance of the computationally exhaustive exact p-value method for XCorr on spectrum datasets containing low-resolution fragmentation information (MS2) around 20-150 times faster. On high-resolution MS2 datasets, the Tailor method with XCorr achieved state-of-the-art performance, and produced more annotations than the well-calibrated Res-ev score around 50-80 times faster.\n\nGraphical TOC Entry\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC=\"FIGDIR/small/831776v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (9K):\norg.highwire.dtl.DTLVardef@10e13cdorg.highwire.dtl.DTLVardef@13638e5org.highwire.dtl.DTLVardef@d17e81org.highwire.dtl.DTLVardef@1c86552_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sulimov, P., Kertesz-Farkas, A.. 2019-11-07. Tailor: non-parametric and rapid score calibration method for database search-based peptide identification in shotgun proteomics. https://doi.org/10.1101/831776

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗

Plate design confounds donor variation and model evaluation in single-cell perturbation screens

Single-cell screens of immune cells from many donors are used to study why individuals respond differently and to benchmark response models. Each donor-perturbation combination typically occupies one well, controls are grouped, and donors share one layout, so technical effects can look biological. Using a 90-cytokine screen, two compound screens, four cohorts and simulations, we show that well effects explain most replicate-control variation in abundant cell types, controls carry a shared offset, and identical control wells differ by hundreds of differentially expressed genes at a standard threshold, about as many as two receptor-negative proteins do. Negative controls, design-aware estimators and perturbation-specific evaluation change the interpretation of four recent analyses. A deep-learning model that scores below a perturbation-agnostic predictor under standard evaluation outperforms a simple baseline on perturbation-specific differences in two of three cell types (significantly in one with stricter controls), but predicts less well the component shared with inert wells.

bioinformatics↗