Biology subjects

Kertesz-Farkas, A.

Publications and source records attributed to Kertesz-Farkas, A..

6 recordsLinked to original sources

bioRxiv2025

Label-free quantification in the Crux toolkit

Ultimately, most tandem mass spectrometry (MS/MS) proteomics experiments aim to not just detect but also quantify the proteins in a given complex sample. Here, we describe an extension to the Crux MS/MS analysis toolkit to enable label-free quantification of peptides. We demonstrate that Cruxs new quantification command, which is modeled after the algorithms implemented in the widely used FlashLFQ software, is both efficient and accurate. In particular, we achieve a 1.9-fold speedup while reducing the memory usage by 26%. The new crux-lfq command is available in Crux v4.3.

bioinformatics↗

bioRxiv2025

Fast and memory efficient searching of large-scale mass spectrometry data using Tide

Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive datasets, when either the number of spectra or the number of proteins being analyzed grows too large. Here we describe enhancements to the Tide search engine that allow it to handle datasets containing >10 million spectra and databases containing >7 billion peptides on commodity hardware. We demonstrate that the new Tide architecture is around 2-7 times faster than the previous version and is now comparable to MSFragger and Sage in speed while requiring much less memory. Tide is open source and is publicly available as pre-compiled binaries for Windows, Linux and Mac.

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bioRxiv2022

The Crux toolkit for analysis of bottom-up tandem mass spectrometry proteomics data

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

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bioRxiv2022

Efficient indexing of peptides for database search using Tide

The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, subject to digestion rules that are under user control. The choice of these digestion parameters, as well as selection of post-translational modifications (PTMs), can dramatically affect the size of the search space and hence the statistical power of the search. The Tide search engine separates the creation of the peptide index from the database search step, thereby saving time by allowing a peptide index to be reused in multiple searches. Here we describe an improved implementation of the indexing component of Tide that consumes around four times less resources (CPU and RAM) than the previous version and can generate arbitrarily large peptide databases, limited by only the amount of available disk space. We use this improved implementation to explore the relationship between database size and the parameters controlling digestion and PTMs, as well as database size and statistical power. Our results can help guide practitioners in proper selection of these important parameters.

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bioRxiv2019

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

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

bioinformatics↗

bioRxiv2019

Test-time augmentation for deep learning-based cell segmentation on microscopy images

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the image for prediction and the results are merged. In this paper we describe incorporating the test-time argumentation prediction method into two major segmentation approaches used in the single-cell analysis of microscopy images, namely semantic segmentation using U-Net and instance segmentation using Mask R-CNN models. Our findings show that even using only simple test-time augmentations, such as rotation or flipping and proper merging methods, will result in significant improvement of prediction accuracy. We utilized images of tissue and cell cultures from the Data Science Bowl (DSB) 2018 nuclei segmentation competition and other sources. Additionally, boosting the highest-scoring method of the DSB with TTA, we could further improve and our method has reached an ever-best score at the DSB.

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