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Software platform by Thermo Fisher Scientific designed for identifying, comparing, and interpreting small molecules in complex biological, environmental, and forensic samples. It uses customizable workflow, known as nodes, to automate mass spectrometry data processing, spectral library searching, and statistical analysis.Compound Discoverer is integrated with SIRIUS (via a custom workflow node) to bridge the gap between high-resolution MS/MS data and confident molecular identification. While Thermo Scientific’s Compound Discoverer excels at library searching and statistical analysis, SIRIUS provides powerful in silico tools to accurately predict molecular formulas, chemical classes, and de novo structures. High-resolution mass spectrometry (HRMS) data analysis software for untargeted metabolomics, lipidomics, and contaminant screening. Utilizes modular workflows to extract features, match spectra against libraries like mzCloud, and confidently identify complex organic compounds.
Software tool as peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. Retention time prediction for peptides.
Web tool and predictive model used by researchers to identify which small protein fragments (peptides) will be presented by human leukocyte antigen (HLA) proteins on the surface of cells. It is heavily used in the development of cancer immunotherapies and personalized
Core offers standard and specialised histology services including tissue processing (murine, human and Drosophila tissue), processing of cellular material (cells, crypt cultures, organoids and spheroids), microtomy, cryo-microtomy, haematoxylin and eosin and specialised tissue stains along with automated immunohistochemical/ immunofluorescence/ in situ hybridisation analysis for specific labelling of individual tissue constituents.
Molecular and analytical core that provides full service genomic library preparation, high-throughput DNA/RNA extractions and small molecule analysis, and serves as a bio-repository of microbes and experimental arthropod models.
Database provides age specific reference templates and segmented brain volumes for participants ranging from 2 weeks to 89 years of age. It is highly utilized for pediatric and lifespan neurostructural studies to prevent misspecifications caused by using adult only templates. Consists of average MRI templates, segmented partial volume estimate volumes for GM, WM, T2W-derived CSF. The database is separated into head-based and brain-based averages. The data are separated by ages in months, years, 6-month, or 5-year intervals. The templates are grouped into first year (2 weeks through 12 months), early childhood (15 months through 4 years), childhood (4 years through 10 years), adolescence (10.5 years through 17.5 years) and adults (18 years through 89 years). Tools for cortical source analysis of EEG and ERP are provided. These tools are based on the average MRI templates, segmenting, and atlases.
Atlas provides a comprehensive framework for dissecting hormonal impact on health and disease. Maps the human endocrine system. Provides a comprehensive map of over 14 million cells to track hormonal signaling. A single cell map uses single cell transcriptomics to map hormone production and action across 47 disease-free human tissues at cellular resolution.
Software toolkit for predicting hormone producing and receiving strength in single cell datasets.
Mass spectrometer for advanced, high-speed, and deep-coverage biological research, specifically targeting proteomics, clinical biomarker discovery, and single-cell analysis. It combines high-resolution accuracy with a rapid scan speed of up to 200 Hz. Powered by the synergy of the high resolution quadrupole mass filter, Thermo Scientific™ Orbitrap™ mass analyzer and the novel Thermo Scientific™ Astral™ mass analyzer. The combination of these three mass analyzers enables the acquisition of quality high resolution accurate mass (HRAM) data with high sensitivity and dynamic range.
Software for collecting, analyzing, and reporting life science data. Used to control inhalation exposure systems, track real-time animal respiration, and calculate precise delivered drug doses in pre-clinical research. It automates aerosol delivery, monitors lung function data, and manages experimental protocols.
Core provides analytic expertise and infrastructure needed to transform complex spatial data into actionable insights to support cancer research, precision population health, learning health system interventions and policy and resource planning at institutional and regional levels.
Core specializes in artificial intelligence-based approaches to elucidate relationships between large imaging, bioenergetics, genomic, and proteomic data sets. Provided services include: 1) foundational training for those getting started with high-performance computing and Arkansas Research Platform (ARP), 2) training and support for the collaborative use of a 508 TB data storage server exclusively maintained for and catering to AIMRC researchers, 3) training for Python programming, basic data mining, and machine learning, 4) training and support for using open-source deep learning based biomedical imaging resources (e.g., ZeroCostDL4Mic and Bioimage Model Zoo), and 5) customized solutions for deep learning based and large foundational models based biomedical imaging analysis, multi-omics data integration and analysis, and quantitative analysis pipelines for large data sets.
Core supports mechanistic human laboratory studies and randomized clinical trials by identifying, coordinating, and providing the scientific expertise, clinical and technical capabilities, and other resources needed for their research projects.
Software tool to predict linear B-cell epitopes (BCEs) from protein sequences. It helps scientists with peptide vaccine design, immuno-diagnostic test development, and antibody production. Used for linear epitope prediction using deep protein sequence embeddings.
Web-based prediction tool used to identify linear B-cell epitopes. Contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.
Software tool used to predict linear B-cell epitopes (BCEs) from protein sequences. It helps scientists find parts of a pathogen that trigger immune responses. Interpretable deep neural network for accurate prediction of linear B-cell epitopes.
Software tool to predict CD4+ T cell epitopes, model MHC-II antigen presentation, and assess immune responses. It helps scientists with vaccine design, cancer neoantigen discovery, and tracking viral mutations.
Software tool for predicting MHC class II antigen immunogenicity through transfer learning. Used to predict whether epitope-MHC class II complex can elicit T cell response.
Software tool that predicts how protein pieces bind to Major Histocompatibility Complex (MHC) molecules. It uses deep learning to process peptide sequences, handle variable lengths, and evaluate both common and rare alleles. Used to predicts peptide-MHC binding. Can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture.
Web multimodal recurrent neural network tool designed to predict HLA-II (Human Leukocyte Antigen class II) peptide ligand presentation. It uses cell HLA alleles, peptide sequences, and source genes to evaluate antigen presentation. Used for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles.