GlycoDash: automated, visually assisted curation of glycoproteomics datasets for large sample numbers

UGC video quality assessment (UGC-VQA) is a challenging research topic due to the high video diversity and limited public UGC quality datasets. State-of-the-art (SOTA) UGC quality models tend to use high complexity models, and rarely discuss the trade-off among complexity, accuracy, and generalizability. We propose a new perspective on UGC-VQA, and show that model complexity may not be critical to the performance, whereas a more diverse dataset is essential to train a better model. We illustrate this by using a light weight model, UVQ-lite, which has higher efficiency and better generalizability (less overfitting) than baseline SOTA models. We also propose a new way to analyze the sufficiency of the training set, by leveraging UVQ’s comprehensive features. Our results motivate a new perspective about the future of UGC-VQA research, which we believe is headed toward more efficient models and more diverse datasets.

Scientists and policymakers are increasingly leveraging complex, multi-scale data from diverse, worldwide sources to understand the causes and consequences of economic development, social stratification, climate change, cultural diversity, and violent conflict. This work frequently requires integrating data across diverse datasets by complex, dynamic categories (e.g., ethnicities, languages, religions, subdistricts). However, different datasets encode corresponding categories in disparate formats and at different resolutions (e.g., Guatemala Indigenous vs. Maya vs. K’iche’). These diverse encodings must be translated across datasets before bringing them together for analysis. At global scales across thousands of categories, the combinatorial complexity creates thorny challenges for manual reconciliation and for transparent documentation and sharing of researcher decisions. There is a need to investigate direct and uncomplicated ways to support search and explore the semantics for complex and diverse datasets.We design and deploy such a tool, CatMapper, to support semantic discovery through exploration and manipulation for large, complex and diverse datasets. CatMapper enables exploring contextual information about specific categories, translating new sets of categories from existing datasets and published studies, identify and integrating novel combinations of datasets for researchers’ custom needs, including automatically generated syntax to merge datasets of interest, and publishing and sharing merging templates for public re-use and open science. CatMapper does not store observational data. Rather, it is a dynamic, interactive dictionary of keys to help users integrate observational data from diverse external datasets in disparate formats, thereby complementing and leveraging a fast-growing ecology of datasets storing observational data. We have conducted heuristic evaluation on CatMapper usability. Results shed lights on enriching semantic data discovery.

Characterization of glycosylation sites for a recombinant IgG1 monoclonal antibody and a CTLA4-Ig fusion protein by liquid chromatography–mass spectrometry peptide mapping

HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data

BackgroundRecent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.ResultsThe work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.ConclusionsThe LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.

A novel framework of a probabilistic-based mixture regression model (PMRM) is presented for alignment of multiple liquid chromatography-mass spectrometry (LC-MS) data with respect to retention time (RT) and mass-to-charge ratio (m/z). The expectation maximization algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation density models. The applicability of PMRM for alignment of LC-MS data is illustrated through three datasets. The performance of our method is compared with other approaches including dynamic time warping, correlation optimized warping, and continuous profile model in terms of coefficient variation of replicate LC-MS data and accuracy in detecting differentially abundant peptides/proteins.

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to newer approaches for measuring changes in peptide/protein abundances. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization, and transformation. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. In this paper, we use a spike-in experiment to evaluate the performance of three software tools in accurately detecting changes in peptide abundances from LC-MS data obtained by a label-free LC-MS method. We observe that tools that incorporate peptide isotope cluster and multiple charge information lead to more accurate difference detection with fewer false positives.

Although feature selection has proven effective in sample class prediction, it is not adequate for identifying leads for potentially useful biomarkers by high-throughput biological data analysis. The large number of equally good predictive sets and the disparity among them reveals the gap between feature selection and biomarker identification. We propose to bridge this gap by a new data mining task, feature cluster selection, which aims to select and group all relevant features in a data set into a small number of coherent clusters. We provide both theoretical framework and empirical formulation for the new problem, and propose the 3M algorithm. Experiments on microarray data show that the algorithm can select highly predictive representative gene sets and discover gene clusters with statistical significance.

Background: Immuno-oncology (IO) therapies, particularly immune checkpoint inhibitors, have transformed cancer care but benefit only a subset of patients. Existing biomarkers such as PD-L1 expression, tumor mutational burden, and microsatellite instability have limited predictive power and are often cancer-type specific. Efforts to identify more generalizable biomarkers are hampered by tumor heterogeneity, restricted access to high-quality datasets, and stringent data privacy regulations, making traditional centralized discovery approaches difficult to scale. To address these challenges, we developed a distributed pipeline for discovering predictive transcriptomic biomarkers across diverse clinical datasets without sharing sensitive patient data. Methods: We developed a scalable, cloud-based distributed pipeline for biomarker discovery that operates across institutions without transferring sensitive patient data. Using curated gene expression profiles, we assessed tumor microenvironment and IO-related signatures in pan-cancer, cancer-specific, and treatment-specific contexts. A multivariable XGBoost model was trained within this distributed framework while maintaining local data governance. Model performance was evaluated against univariable signature-based models and multivariable models trained locally on individual datasets. Results: We implemented a distributed meta-integration pipeline to 18 datasets comprising 967 patients across seven cancer types treated with PD-1/PD-L1, CTLA-4, or combination immunotherapies. This analysis identified biomarkers specific to lung cancer and CTLA-4 therapy that were not detected in pan-cancer analyses, highlighting the value of context-specific associations. Several gene expression signatures were consistently linked to progression-free survival and treatment response in both pan-cancer and melanoma datasets, with notable overlap between PD-1/PD-L1 and dual checkpoint blockade therapies. Distributed multivariable models outperformed locally trained models on average, indicating improved generalizability, though performance often depended on cancer type and dataset composition, with univariable signature-based models outperforming in some settings. Conclusion: This study demonstrates a distributed framework for IO biomarker discovery that preserves patient privacy and facilitates collaboration across institutions. Our findings highlight the value of multi-institutional, diverse datasets in refining context-specific biomarkers and advancing precision IO therapies. Citation Format: Farnoosh Abbas-Aghababazadeh, Kewei Ni, Kevin (Xin) Wang, Sisira Kadambat Nair, Nasim BondarSahebi, John Stagg, Benjamin Haibe-Kains. Distributed Transcriptomic Modeling for Biomarker Discovery in Immuno-Oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B044.

A simple, rapid, and sensitive liquid chromatography-mass spectrometry (LC-MS) method was successfully developed for the determination of three chloro-s-triazine herbicides, viz. atrazine, simazine, and propazine, two thiomethyl-s-triazine ones, viz. ametryn and prometryn, and prometon in fruits and vegetables. The instrumental parameters affecting LC separation and MS detection were investigated and the best conditions were selected. Samples were extracted and purified by dispersive solid-phase extraction (dispersive-SPE) with a primary-secondary amine sorbent. Three typical representative samples (luffa, broad bean, and grape) were selected to investigate the effect of different matrices on pesticide recoveries and assay precision after spiking at 0.05 mg/kg. Matrix composition did not interfere significantly with the determination of these pesticides. The recoveries were, with a few exceptions, in the range of 80-110% with relative standard deviations less than 10% (n = 5). In addition, this method was also applied to 21 different kinds of agriculture products collected from the local market, and two of the six triazines could be detected and quantified.

Antimicrobial resistance (AMR) is considered a critical threat to public health, and genomic/metagenomic investigations featuring high-throughput analysis of sequence data are increasingly common and important. We previously introduced MEGARes, a comprehensive AMR database with an acyclic hierarchical annotation structure that facilitates high-throughput computational analysis, as well as AMR++, a customized bioinformatic pipeline specifically designed to use MEGARes in high-throughput analysis for characterizing AMR genes (ARGs) in metagenomic sequence data. Here, we present MEGARes v3.0, a comprehensive database of published ARG sequences for antimicrobial drugs, biocides, and metals, and AMR++ v3.0, an update to our customized bioinformatic pipeline for high-throughput analysis of metagenomic data (available at MEGLab.org). Database annotations have been expanded to include information regarding specific genomic locations for single-nucleotide polymorphisms (SNPs) and insertions and/or deletions (indels) when required by specific ARGs for resistance expression, and the updated AMR++ pipeline uses this information to check for presence of resistance-conferring genetic variants in metagenomic sequenced reads. This new information encompasses 337 ARGs, whose resistance-conferring variants could not previously be confirmed in such a manner. In MEGARes 3.0, the nodes of the acyclic hierarchical ontology include 4 antimicrobial compound types, 59 resistance classes, 233 mechanismsand 1448 gene groups that classify the 8733 accessions.

Human sweat can be noninvasively collected and used as a media for diagnosis of certain diseases as well as for drug detection. However, because of very low concentrations of endogenous metabolites present in sweat, metabolomic analysis of sweat with high coverage is difficult, making it less widely used for metabolomics research. In this work, a high-performance method for profiling the human sweat submetabolome based on chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is reported. Sweat was collected using a gauze sponge style patch, extracted from the gauze by centrifugation, and then derivatized using CIL. Differential (12)C- and (13)C-dansylation labeling was used to target the amine/phenol submetabolome. Because of large variations in the total amount of sweat metabolites in individual samples, sample amount normalization was first performed using liquid chromatography with UV detection (LC-UV) after dansylation. The (12)C-labeled individual sample was then mixed with an equal amount of (13)C-labeled pooled sample. The mixture was subjected to LC-MS analysis. Over 2707 unique metabolites were detected across 54 sweat samples collected from six individuals with an average of 2002 ± 165 metabolites detected per sample from a total of 108 LC-MS runs. Using a dansyl standard library, we were able to identify 83 metabolites with high confidence; many of them have never been reported to be present in sweat. Using accurate mass search against human metabolome libraries, we putatively identified an additional 2411 metabolites. Uni- and multivariate analyses of these metabolites showed significant differences in the sweat submetabolomes between male and female, as well as between early and late exercise. These results demonstrate that the CIL LC-MS method described can be used to profile the human sweat submetabolome with high metabolomic coverage and high quantification accuracy to reveal metabolic differences in different sweat samples, thereby allowing the use of sweat as another human biofluid for comprehensive and quantitative metabolomics research.

A simple to construct and operate, "dip-in" electromembrane extraction (EME) probe directly coupled to electrospray ionization-mass spectrometry (ESI-MS) for rapid extraction and real time analysis of various analytes was developed. The setup demonstrated that EME-MS can be used as a viable alternative to conventional protein precipitation followed by liquid chromatography-mass spectrometry (LC-MS) for studying drug metabolism. Comparison of EME-MS with LC-MS for drug metabolism analysis demonstrated for the first time that real time extraction of analytes by EME is possible. Metabolism kinetics were investigated for three different drugs: amitriptyline, promethazine, and methadone. By comparing the EME-MS extraction profiles of the drug substances and formed drug metabolites with the metabolism profiles obtained by conventional protein precipitation followed by LC-MS good correlation was obtained with only very limited time delay in the extraction. The results indicate that, by tuning the electromembrane properties, for example, by optimizing the extraction voltage, extremely fast extraction kinetics can be obtained. A metabolic profile could be generated while the drug was metabolized offering a significant time saving as compared to conventional LC-MS where laborious protein precipitation or other sample pretreatments are required before analysis. This makes the developed EME-MS setup a highly promising sample preparation method for various kinds of applications where fast and real-time analysis of analytes is of interest.

High-throughput data analysis and data integration for vaccine trials

The experiment was designed to evaluate the influence of dietary supplementation with L-Citrulline (L-Cit) on reproductive outcomes, serum reproductive hormone profiles, ruminal and intestinal microbial communities, serum metabolites, and their metabolic associations in Simmental cows, thereby providing a theoretical basis for its application in ruminant production. All cows were screened by vaginal mucus microscopy (no pathogenic bacteria detected) and B-ultrasound examination (no ovarian cysts or uterine inflammation observed) to confirm the absence of reproductive disorders. A total of 240 multiparous Simmental cows, 3–4 years of age with an average body weight of 470 ± 15 kg, were randomly allocated into three groups of 80 animals each: a control group, Group I, and Group II. The control group received only the basal diet, whereas Group I and Group II were provided with the basal diet supplemented with 7 g/d and 14 g/d of L-Cit per cow, respectively. Day 0 of the trial was defined by the first intramuscular injection of prostaglandin (PG, 0.4 mg per cow), after which L-Cit supplementation commenced at 72-h intervals. On day 10, a second PG injection (0.4 mg per cow) was administered, and supplementation was discontinued on day 11. Estrus expression was monitored, and pregnancy was assessed on day 35 post-insemination using B-mode ultrasonography. During the experimental period, six cows were randomly selected from each group. Blood samples were collected from the coccygeal vein prior to the morning feeding on days 1 and 11 using plain tubes (without anticoagulant). The samples were centrifuged at 720 × g (approximately 3,000 rpm for 15 min) to separate the serum. The obtained serum was stored at −20 °C for subsequent analysis of reproductive hormones. In addition, blood, rumen fluid, and fecal samples were obtained 10 h after mounting to support untargeted metabolomic profiling and microbial community analysis. Compared with the control group, the estrus rate in experimental Group I increased by 12.5% (p < 0.05). In experimental Group II, serum concentrations of gonadotropin-releasing hormone (GnRH) and follicle-stimulating hormone (FSH) measured 10 h after cows accepted mounting rose by 15.79% (p < 0.05) and 35.71% (p < 0.01), respectively, relative to the control group. Analysis of 16S rRNA sequences revealed significant differences in ruminal microbial taxa, including Verrucomicrobiota, Lentisphaeria, Oligosphaeraceae, vadinBE97_g_norank, vadinBE97, Eubacterium_ruminantium_group, and Prevotellaceae_g_norank. In the intestine, significant differences were observed in Lachnospiraceae, Lachnospirales, Marvinbryantia, Desulfovibrionia, Desulfovibrionaceae, and Desulfobacterota. KEGG enrichment analysis based on Liquid Chromatography-Mass Spectrometry (LC–MS) data indicated upregulation of the arginine biosynthesis pathway in the experimental groups, whereas metabolites associated with the tricarboxylic acid (TCA) cycle were notably elevated in experimental Group I. Within the experimental framework, dietary supplementation with L-Cit combined with the two-shot PG synchronization protocol reshaped the ruminal microbial community structure of Simmental cows, improved glucose metabolic efficiency and xenobiotic degradation capacity, and promoted the synthesis and release of reproductive hormones. Consequently, estrus and conception rates were enhanced.