The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.
- MeSH
- analýza dat MeSH
- benchmarking * MeSH
- proteiny MeSH
- proteomika * MeSH
- průběh práce MeSH
- software MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- benchmarking * MeSH
- lidé MeSH
- nádory * epidemiologie prevence a kontrola MeSH
- prevalence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The aim was to provide benchmarks and investigate contribution of start and turn performances in the European Short-Course Swimming Championship. Over all race distances, 932 individual races of male competitors were video captured and the start and turn performances were analysed. Start and turn performances contributed up to 26% and 56% of the total race time. Analysis of variance showed that the 15 m start times were faster for Freestyle and Butterfly (p < 0.001) compared to the other swimming strokes. In-water starts (Backstroke) were slower at the 5 m mark compare to on-block starts (p < 0.001). Tumble turns were faster than open turns (p < 0.001). Multiple linear regression analysis explained 82-97% of total variance in the race results with a decreasing effect of start performance for the longer distance races. Turn performance affected the results across all race distances (p < 0.001). The benchmarks and percentiles provide comparative data for swimmers of different performance levels. Considering the large contribution of start and turn performance to race time and the high effect in the regression model, training regimes that are mainly based on conditioning of free-swimming should be reconsidered.
- MeSH
- benchmarking * MeSH
- biomechanika MeSH
- kompetitivní chování MeSH
- lidé MeSH
- plavání MeSH
- sportovní výkon * MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Accurate estimates of intermolecular interaction energy, ΔE, are crucial for modeling the properties of organic electronic materials and many other systems. For a diverse set of 50 dimers comprising up to 50 atoms (Set50-50, with 7 of its members being models of single-stacking junctions), benchmark ΔE data were compiled. They were obtained by the focal-point strategy, which involves computations using the canonical variant of the coupled cluster theory with singles, doubles, and perturbative triples [CCSD(T)] performed while applying a large basis set, along with extrapolations of the respective energy components to the complete basis set (CBS) limit. The resulting ΔE data were used to gauge the performance for the Set50-50 of several density-functional theory (DFT)-based approaches, and of one of the localized variants of the CCSD(T) method. This evaluation revealed that (1) the proposed "silver standard" approach, which employs the localized CCSD(T) method and CBS extrapolations, can be expected to provide accuracy better than two kJ/mol for absolute values of ΔE, and (2) from among the DFT techniques, computationally by far the cheapest approach (termed "ωB97X-3c/vDZP" by its authors) performed remarkably well. These findings are directly applicable in cost-effective yet reliable searches of the potential energy surfaces of noncovalent complexes.
- MeSH
- benchmarking * MeSH
- dimerizace MeSH
- elektronika * MeSH
- fyzikální jevy MeSH
- polymery MeSH
- Publikační typ
- časopisecké články MeSH
The success of bottom-up proteomic analysis frequently depends on the efficient removal of contaminants from protein or peptide samples before LC-MS/MS. For a peptide clean-up workflow, single-pot solid-phase-enhanced peptide sample preparation on carboxylate-modified paramagnetic beads (termed SP2) was evaluated for sodium dodecyl sulfate or polyethylene glycol removal from Arabidopsis thaliana tryptic peptides. The robust and efficient 40-min SP2 protocol, tested for 10-ng, 250-ng, and 10-μg peptide samples, was proposed and benchmarked thoroughly against the ethyl acetate extraction protocol. The SP2 protocol on carboxylated magnetic beads proved to be the most robust approach, even for the simultaneous removal of massive sodium dodecyl sulfate (SDS) and polyethylene glycol (PEG) contaminations from AT peptide samples in respect of the LC-MS/MS data outperforming ethyl acetate extraction.
BACKGROUND AND PURPOSE: In patients with recurrent ventricular tachycardia (VT), STereotactic Arrhythmia Radioablation (STAR) shows promising results. The STOPSTORM.eu consortium was established to investigate and harmonise STAR treatment in Europe. The primary goals of this benchmark study were to standardise contouring of organs at risk (OAR) for STAR, including detailed substructures of the heart, and accredit each participating centre. MATERIALS AND METHODS: Centres within the STOPSTORM.eu consortium were asked to delineate 31 OAR in three STAR cases. Delineation was reviewed by the consortium expert panel and after a dedicated workshop feedback and accreditation was provided to all participants. Further quantitative analysis was performed by calculating DICE similarity coefficients (DSC), median distance to agreement (MDA), and 95th percentile distance to agreement (HD95). RESULTS: Twenty centres participated in this study. Based on DSC, MDA and HD95, the delineations of well-known OAR in radiotherapy were similar, such as lungs (median DSC = 0.96, median MDA = 0.1 mm and median HD95 = 1.1 mm) and aorta (median DSC = 0.90, median MDA = 0.1 mm and median HD95 = 1.5 mm). Some centres did not include the gastro-oesophageal junction, leading to differences in stomach and oesophagus delineations. For cardiac substructures, such as chambers (median DSC = 0.83, median MDA = 0.2 mm and median HD95 = 0.5 mm), valves (median DSC = 0.16, median MDA = 4.6 mm and median HD95 = 16.0 mm), coronary arteries (median DSC = 0.4, median MDA = 0.7 mm and median HD95 = 8.3 mm) and the sinoatrial and atrioventricular nodes (median DSC = 0.29, median MDA = 4.4 mm and median HD95 = 11.4 mm), deviations between centres occurred more frequently. After the dedicated workshop all centres were accredited and contouring consensus guidelines for STAR were established. CONCLUSION: This STOPSTORM multi-centre critical structure contouring benchmark study showed high agreement for standard radiotherapy OAR. However, for cardiac substructures larger disagreement in contouring occurred, which may have significant impact on STAR treatment planning and dosimetry evaluation. To standardize OAR contouring, consensus guidelines for critical structure contouring in STAR were established.
- MeSH
- benchmarking MeSH
- komorová tachykardie * radioterapie chirurgie MeSH
- koronární cévy MeSH
- lidé MeSH
- plánování radioterapie pomocí počítače * metody MeSH
- srdce MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Acute ischemic stroke (AIS) due to anterior circulation tandem lesion (TL) remains a technical and clinical challenge for endovascular treatment (EVT). Conflicting results from observational studies and missing evidence from the randomized trials led us to report a recent real-world multicenter clinical experience and evaluate possible predictors of good outcome after EVT. METHODS: We analyzed all AIS patients with TL enrolled in the prospective national study METRICS (Mechanical Thrombectomy Quality Indicators Study in Czech Stroke Centers). A good 3-month clinical outcome was scored as 0-2 points in modified Rankin Scale (mRS), achieved recanalization using the Thrombolysis In Cerebral Infarction (TICI) scale and symptomatic intracerebral hemorrhage (sICH) according to the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) criteria. RESULTS: Of 1178 patients enrolled in METRICS, 194 (19.2%) (59.8% males, mean age 68.7±11.5 years) were treated for TL. They did not differ in mRS 0-2 (48.7% vs 46.7%; p=0.616), mortality (17.3% vs 22.7%; p=0.103) and sICH (4.7% vs 5.1%; p=0.809) from those with single occlusion (SO). More TL patients with prior intravenous thrombolysis (IVT) reached TICI 3 (70.3% vs 50.8%; p=0.012) and mRS 0-2 (55.4% vs 34.4%; p=0.007) than those without IVT. No difference was found in the rate of sICH (6.2% vs 1.6%; p=0.276). Multivariate logistic regression analysis showed prior IVT as a predictor of mRS 0-2 after adjustment for potential confounders (OR 3.818, 95% CI 1.614 to 9.030, p=0.002). CONCLUSION: Patients with TL did not differ from those with SO in outcomes after EVT. TL patients with prior IVT had more complete recanalization and mRS 0-2 and IVT was found to be a predictor of good outcome after EVT.
- MeSH
- benchmarking MeSH
- cerebrální krvácení etiologie MeSH
- cévní mozková příhoda * diagnostické zobrazování chirurgie MeSH
- endovaskulární výkony * metody MeSH
- fibrinolytika MeSH
- ischemická cévní mozková příhoda * diagnostické zobrazování chirurgie MeSH
- ischemie mozku * diagnostické zobrazování terapie etiologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- prospektivní studie MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- trombektomie škodlivé účinky MeSH
- trombolytická terapie metody MeSH
- výsledek terapie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.
- MeSH
- benchmarking * MeSH
- jizva MeSH
- lidé MeSH
- nemoci převodního systému srdečního MeSH
- počítačová simulace MeSH
- srdeční síně * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. RESULTS: Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks . CONCLUSIONS: Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.
