Suite Of Analysis Tools Research Articles

IModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets.

iModulons-sets of co-expressed genes identified through independent component analysis (ICA) of high-quality transcriptomic datasets-provide an unbiased, modular view of an organism's transcriptional regulatory network. Established in 2020, iModulonDB (iModulonDB.org) serves as a centralized repository of curated iModulon sets, enabling users to explore iModulons and download the associated transcriptomic data. This update reflects a significant expansion of the database-19 new ICA decompositions (+633%) spanning 8925 expression profiles (+1370%), 503 studies (+2290%)and 12 additional organisms (+400%)-and introduces new features to help scientists decipher the mechanisms governing prokaryotic transcriptional regulation. To facilitate comprehension of the underlying expression profiles, the updated user-interface displays essential information about each data-generating study (e.g. the experimental conditions and publication abstract). Dashboards now include condition-specific coloring and highlight data generated from genetically perturbed strains, enabling users to rapidly interpret disruptions in transcriptional regulation. New interactive graphs rapidly convey omics-derived indicators (e.g. the explained variance of ICA decompositions, genetic overlap between iModulons and regulons). Direct links to operon diagrams (BioCyc) and protein-protein interaction networks (STRING) provide users with seamless access to external resources for further assessment of iModulons. Lastly, a new suite of search-driven and species-wide analysis tools promotes user-engagement with iModulons, reinforcing iModulonDB's role as a dynamic, interactive knowledgebase of prokaryotic transcriptional regulation.

BioPred: An R package for biomarkers analysis in precision medicine.

The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules (ITR) to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development. The package is available at CRAN and https://github.com/deeplearner0731/BioPred. Supplementary data are available at Bioinformatics online.

The BioRECIPE Knowledge Representation Format.

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.

Real-time plasma equilibrium reconstruction and shape control for the MAST Upgrade tokamak

Real-time magnetic control has been developed to deliver precise control of multiple plasma shape parameters for advanced divertor configurations, including double-null, Super-X, X-point target and X-divertor for the first time on the MAST Upgrade (MAST-U) spherical tokamak. Successful real-time magnetic equilibrium control of different plasma shape variables has been accomplished in the 2022–2023 MAST-U experimental campaign for the advanced MAST-U divertor configurations. Application of the MAST-U boundary reconstruction algorithm, LEMUR, is described and compared with off-line equilibrium reconstruction and diagnostic measurements. The process of design and verification of the axisymmetric magnetic control schemes using a suite of control analysis tools (known collectively as TokSys) is also described.

Algorithm 1044: PyGenStability, a Multiscale Community Detection Framework with Generalized Markov Stability

We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualization tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden algorithm. The package includes automatic detection of robust graph partitions and allows the flexibility to choose quality functions for weighted undirected, directed, and signed graphs and to include other user-defined quality functions.

Computational toolbox makes microneurography analysis techniques widely available

Open-source, comprehensive suite of analysis tools provides standardized data and analytics, increases potential for collaboration.

Two-Dimensional Projected-Momentum Covariance Mapping for Coulomb Explosion Imaging.

We introduce projected-momentum covariance mapping, an extension of recoil-frame covariance mapping for 2D ion imaging studies. By considering the two-dimensional projection of the ion momenta as recorded by the detector, one opens the door to a complex suite of analysis tools adapted from three-dimensional momentum imaging studies. This includes the use of different frames of reference to unravel the dynamics of fragmentation and the application of fragment momentum constraints to isolate specific fragmentation channels. The technique is demonstrated on data from a two-dimensional ion imaging study of the Coulomb explosion of the cis and trans isomers of 1,2-dichloroethene, following strong-field ionization by an intense near-infrared femtosecond laser pulse. Classical simulations are used to guide the interpretation of projected-momentum covariance maps. The results offer a detailed insight into the distinct Coulomb explosion dynamics for this pair of isomers and lay the groundwork for future time-resolved studies of photoisomerization dynamics in this molecular system.

Integrating Digital Editions and Methods for Text Editing and Analysis in Undergraduate Literary Studies

This article evaluates the integration of digital editions, computational text analysis and digital scholarly editing in the context of an introductory undergraduate course on Italian literature and digital humanities taught at a US university. It offers specific examples of employing the apparatus of several digital platforms dedicated to the study of foundational authors in the Italian literary tradition (Dante, Petrarch, Boccaccio and Leopardi), and of gaining familiarity with a suite of digital tools for text analysis and editing, namely Voyant Tools, Recogito, Oxygen, Gephi, Transkribus Lite and OpenRefine. The discussion of digital project interfaces examines the student user experience of different design approaches, while the illustrations of tool exercises explore how these could support the close attention to a text and facilitate the navigation between its micro and macro frameworks of interpretation. The article furthermore suggests that digital text analysis could reinforce student appreciation of the signifying value of textual form and genre, and that the pedagogical method of digital text editing creates opportunities for situated learning. In conclusion, it argues that the academic work of students at the undergraduate level could be harnessed by the scaffolded methods of faculty-led digital research projects and contribute to the creation of public knowledge.

MMORF—FSL’s MultiMOdal Registration Framework

Abstract We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.

Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

(Invited) Graphene Ion Blocking Layers for Robust Metal-Halide Perovskite Heterostructures

Despite being only one atomic layer thick, a continuous monolayer of graphene should be completely impermeable to atoms and ions. As such, graphene monolayers could potentially be employed as ion blocking layers in numerous technologies where charge and/or energy transport across a key interface is desirable, but ionic or atomic transport is not. As a key example, the development of metal-halide perovskite/perovskite heterostructures is hindered by rapid interfacial halide diffusion leading to mixed alloys rather than sharp interfaces. This halide diffusion hinders the creation of tailored interfaces that funnel energy or charge in specified directions within energy technologies such as photovoltaic solar cells or light-emitting diodes. To circumvent this issue, we developed an ion-blocking layer consisting of single layer graphene (SLG) deposited between the metal-halide perovskite layers and demonstrate that it effectively blocks anion diffusion in perovskite/perovskite heterostructures. We study these interfaces with a suite of spectroscopy and elemental analysis tools to demonstrate that halides do not diffuse across the graphene-modified interface, whereas control samples without the SLG show rapid homogenization of the halides and loss of the sharp interface. The SLG has little electronic impact on the band alignment and optical properties of the individual semiconductors, as supported by density functional theory calculations, facilitating photoinduced energy transfer across the abrupt heterointerface and light-emitting diodes with no evidence of ion-diffusion during operation. Our results demonstrate that monolayer graphene can be used as an ion blocking layer in a variety of different heterostructures for advanced opto-electronic and electrochemical applications. Further, it establishes a route towards robust perovskite/perovskite heterostructures that are difficult to achieve by other means.

Fish Catch Survey and Analysis – An online application for deriving measures and indicators for fish stock assessment

As fish are a natural resource, it is important to balance their use and protection. To comprehend the status of fisheries and to keep sustainable harvest levels to prevent the fish population from declining over time, reliable data on both recent and historical catches are crucial. The monitoring and assessment of marine fishery resources have been the primary mandate of the Central Marine Fisheries Research Institute of the Indian Council of Agricultural Research (ICAR-CMFRI) since its inception in 1947. Accurate and consistent knowledge about how a fishery is doing, as well as what species and how many are being captured and where they are captured, requires precise data collection, fast and advanced reporting, processing and analysis, and efficient mechanisms to disseminate the results to enable near real-time assessment. Stratified multistage random sampling design (SMRSD), a unique and robust system developed by the institute for the estimation of fish catch and effort over a long period used multiple computer programs built in-house for processing data. The area covering the 1269 fish landing centres along the 6,068-km coastline of India is spatially stratified, with each maritime state divided into suitable nonoverlapping zones based on fishing intensity and geographical considerations. In order to collect data on a real-time basis from the fish landing centres using electronic devices and process it, ICAR-CMFRI developed a web based application software called Fish Catch Survey and Analysis. It is a user-friendly interface with a variety of data entry options, and a suite of analysis tools. It includes species-wise, gear-wise and catch-per-unit-effort analysis also. Researchers and decision-makers can develop appropriate measures and indicators for fish stock assessment using FCSA.

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques

BackgroundThis study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification of risk factors for timely diagnosis and improved patient outcomes. The researchers conducted a literature review to address previous studies' limitations in using machine learning to assess CVD risk factors.MethodsThis study analyzed data from 1024 patients to identify the significant CVD risk factors based on sex. The data comprising 13 features, such as demographic, lifestyle, and clinical factors, were obtained from the UCI repository and preprocessed to eliminate missing information. The analysis was performed using principal component analysis (PCA) and latent class analysis (LCA) to determine the major CVD risk factors and to identify any homogeneous subgroups between male and female patients. Data analysis was performed using XLSTAT Software. This software provides a comprehensive suite of tools for Data Analysis, Machine Learning, and Statistical Solutions for MS Excel.ResultsThis study showed significant sex differences in CVD risk factors. 8 out of 13 risk factors affecting male and female patients found that males and females share 4 of the eight risk factors.Identified latent profiles of CVD patients, suggesting the presence of subgroups among CVD patients. These findings provide valuable insights into the impact of sex differences on CVD risk factors. Moreover, they have important implications for healthcare professionals, who can use this information to develop individualized prevention and treatment plans. The results highlight the need for further research to elucidate these disparities better and develop more effective CVD prevention measures.ConclusionsThe study explored the sex differences in the CVD risk factors and the presence of subgroups among CVD patients using ML techniques. The results revealed sex-specific differences in risk factors and the existence of subgroups among CVD patients, thus providing essential insights for personalized prevention and treatment plans. Hence, further research is necessary to understand these disparities better and improve CVD prevention.

The T cell receptor repertoire reflects the early dynamics of the immune response to vaccination

Abstract Early ascertainment of the immune response to vaccinations is important. The T cell receptor (TCR) is a promising target to achieve this, with the complete TCR repertoire in PBMCs reflecting the T cells present in an individual. To this end, we developed Tseek, an unbiased and accurate method for profiling the TCR repertoire by sequencing the TCR α and β chains and developing a suite of tools for repertoire analysis. We explored the T cell response to both the COVID-19 mRNA vaccine (n=9) and the seasonal inactivated Influenza vaccine (n=5) at several time points. Neutralizing antibody titers were also measured in the covid vaccine samples. The COVID-19 vaccine elicited a broad T cell response involving multiple expanded clones, whereas the Influenza vaccine elicited a narrower response involving fewer clones. Distinct T cell clones responded at different time points over a month, providing temporal details lacking in the antibody measurements. The physical symptoms following administration of the vaccinations were not indicative of the TCR/antibody responses while the TCR responses broadly presaged the antibody responses. Additionally, the TCR repertoire is akin to a fingerprint: donors of blood samples taken years apart could be identified from the TCR repertoire. TCR repertoire profiling shows great promise as an early and sensitive measure of the adaptive immune response to vaccination, which can help improve immunogen selection and optimize vaccine dosage and spacing between doses.

Modelling, design and simulation of plasma magnetic control for the Spherical Tokamak for Energy Production (STEP)

Magnetic controller performance requirements and design solutions for the Spherical Tokamak for Energy Production (STEP) are driven by the need to produce equilibrium and scenario trajectories, maintaining steady plasma vertical stabilization and shape control for a period of thousands of seconds or longer while avoiding contact with the plasma-facing components (PFCs). Axisymmetric magnetic control schemes including vertical stabilization, plasma current control, plasma shape control and poloidal field coil current control for the STEP Prototype Reactor (SPR) concept are being developed using a suite of control analysis tools (known collectively as TokSys) supporting the integrated plasma control design process. The vertical growth rate based on a linear rigid plasma response model in TokSys is used for assessing the controllability of the vertical instability in SPR. TokSys closed-loop simulations with the axisymmetric non-linear, free-boundary evolution code, GSevolve, are performed for the assessment, identification, and verification of algorithm implementation and controller performance for the various axisymmetric control systems. Dynamic control performance and controllability are shown to be consistent with noise-affected scenario requirements under reasonable power supply and sensor performance assumptions.

SpliceTools, a suite of downstream RNA splicing analysis tools to investigate mechanisms and impact of alternative splicing.

As a fundamental aspect of normal cell signaling and disease states, there is great interest in determining alternative splicing (AS) changes in physiologic, pathologic, and pharmacologic settings. High throughput RNA sequencing and specialized software to detect AS has greatly enhanced our ability to determine transcriptome-wide splicing changes. Despite the richness of this data, deriving meaning from sometimes thousands of AS events is a substantial bottleneck for most investigators. We present SpliceTools, a suite of data processing modules that arms investigators with the ability to quickly produce summary statistics, mechanistic insights, and functional significance of AS changes through command line or through an online user interface. Utilizing RNA-seq datasets for 186 RNA binding protein knockdowns, nonsense mediated RNA decay inhibition, and pharmacologic splicing inhibition, we illustrate the utility of SpliceTools to distinguish splicing disruption from regulated transcript isoform changes, we show the broad transcriptome footprint of the pharmacologic splicing inhibitor, indisulam, we illustrate the utility in uncovering mechanistic underpinnings of splicing inhibition, we identify predicted neo-epitopes in pharmacologic splicing inhibition, and we show the impact of splicing alterations induced by indisulam on cell cycle progression. Together, SpliceTools puts rapid and easy downstream analysis at the fingertips of any investigator studying AS.

TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes.

Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport.org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www.membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.

Mouse Phenome Database: towards a more FAIR-compliant and TRUST-worthy data repository and tool suite for phenotypes and genotypes.

The Mouse Phenome Database (MPD; https://phenome.jax.org; RRID:SCR_003212), supported by the US National Institutes of Health, is a Biomedical Data Repository listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry. As an increasingly FAIR-compliant and TRUST-worthy data repository, MPD accepts phenotype and genotype data from mouse experiments and curates, organizes, integrates, archives, and distributes those data using community standards. Data are accompanied by rich metadata, including widely used ontologies and detailed protocols. Data are from all over the world and represent genetic, behavioral, morphological, and physiological disease-related characteristics in mice at baseline or those exposed to drugs or other treatments. MPD houses data from over 6000 strains and populations, representing many reproducible strain types and heterogenous populations such as the Diversity Outbred where each mouse is unique but can be genotyped throughout the genome. A suite of analysis tools is available to aggregate, visualize, and analyze these data within and across studies and populations in an increasingly traceable and reproducible manner. We have refined existing resources and developed new tools to continue to provide users with access to consistent, high-quality data that has translational relevance in a modernized infrastructure that enables interaction with a suite of bioinformatics analytic and data services.

Pacific Biosciences Fusion and Long Isoform Pipeline for Cancer Transcriptome–Based Resolution of Isoform Complexity

Genomic profiling using short-read sequencing has utility in detecting disease-associated variation in both DNA and RNA. However, given the frequent occurrence of structural variation in cancer, molecular profiling using long-read sequencing improves the resolution of such events. For example, the Pacific Biosciences long-read RNA-sequencing (Iso-Seq) transcriptome protocol provides full-length isoform characterization, discernment of allelic phasing, and isoform discovery, and identifies expressed fusion partners. The Pacific Biosciences Fusion and Long Isoform Pipeline (PB_FLIP) incorporates a suite of RNA-sequencing software analysis tools and scripts to identify expressed fusion partners and isoforms. In addition, sequencing of a commercial reference (Spike-In RNA Variants) with known isoform complexity was performed and demonstrated high recall of the Iso-Seq and PB_FLIP workflow to benchmark our protocol and analysis performance. This study describes the utility of Iso-Seq and PB_FLIP analysis in improving deconvolution of complex structural variants and isoform detection within an institutional pediatric and adolescent/young adult translational cancer research cohort. The exemplar case studies demonstrate that Iso-Seq and PB_FLIP discover novel expressed fusion partners, resolve complex intragenic alterations, and discriminate between allele-specific expression profiles.

GDSC SMLM: Single-molecule localisation microscopy software for ImageJ.

Single-molecule localisation microscopy (SMLM) uses software to extract super-resolved positions from microscope images of fluorescent molecules. These localisations can then be used to render super-resolution images or analysed to extract information about molecular behaviour. The GDSC SMLM software provides a set of tools for analysing SMLM data in a single cross-platform environment. The software identifies fluorescent molecules in raw microscope images and localises their positions using stages of spot detection, spot fitting and spot rejection. The resulting localisation data set can then be visualised, cropped and filtered. A suite of downstream analysis tools enable the user to perform single-particle tracking, cluster analysis and drift correction. In addition, GDSC SMLM also provides utility tools that enable modelling of EM-CCD and sCMOS cameras as well as point spread functions (PSFs) for data simulation. The software is written in Java and runs as a collection of plugins for the ImageJ software.