Command Line Research Articles

LUNAR: Automated Input Generation and Analysis for Reactive LAMMPS Simulations.

Generating simulation-ready molecular models for the LAMMPS molecular dynamics (MD) simulation software package is a difficult task and impedes the more widespread and efficient use of MD in materials design and development. Fixed-bond force fields generally require manual assignment of atom types, bonded interactions, charges, and simulation domain sizes. A new LAMMPS pre- and postprocessing toolkit (LUNAR) is presented that efficiently builds molecular systems for LAMMPS. LUNAR automatically assigns atom types, generates bonded interactions, assigns charges, and provides initial configuration methods to generate large molecular systems. LUNAR can also incorporate chemical reactivity into simulations by facilitating the use of the REACTER protocol. Additionally, LUNAR provides postprocessing for free volume calculations, cure characterization calculations, and property predictions from LAMMPS thermodynamic outputs. LUNAR has been validated via building and simulation of pure epoxy and cyanate ester polymer systems with a comparison of the corresponding predicted structures and properties to benchmark values, including experimental results from the literature. LUNAR provides the tools for the computationally driven development of next-generation composite materials in the Integrated Computational Materials Engineering (ICME) and Materials Genome Initiative (MGI) frameworks. LUNAR is written in Python with the usage of NumPy and can be used via a graphical user interface, a command line interface, or an integrated design environment. LUNAR is freely available via GitHub.

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BackgroundPan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.ResultsHere, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.ConclusionsEntourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

Exploring Trainees’ Behaviour in Hands-on Cybersecurity Exercises Through Data Mining

Despite the rising number of cybersecurity professionals, the demand for more experts in this field is still substantial. Cybersecurity professionals must also possess up-to-date knowledge and skills to counter cybersecurity threats’ dynamicity and rapidly evolving nature. Hands-on cybersecurity training is mandatory to practice various tools and improve one’s technical cybersecurity skills. Generally, an interactive learning environment is set, where trainees perform sophisticated tasks by accessing complete operating systems, applications, and networks. One of the main challenges that cybersecurity organizations are facing today is the generation of massive data through practice exercises. So, it becomes a problem to convert this data into knowledge to improve the overall quality of the learning system. The large amount of interaction data and its complexity also limit us to do automated analysis. Thus, these challenges for cybersecurity learners can be addressed through appropriate educational data analysis by having insights or testing hypotheses or models on a proper dataset. Revealing the patterns, rules, item sets and time taken by trainees while using any command line tool could help the trainer to assess the trainees and to provide feedback. Therefore, in this paper we are analyzing the frequency patterns and timing information captured from the trainees’ command line log to reveal their solving techniques, easy and struggling stages, slipups, and individual performance. Through our study, we aim to show how education and training providers can foresee learners who are less likely to succeed in a task or exhibit low performance, which can impede learning proficiency. With this knowledge, organizations and trainers can identify trainees who require additional attention or support. It may also be able to identify elements related to an organization like training aids, training methodology, etc. that need improvement. This study demonstrates the utility of data-mining techniques, specifically rule mining and sequential mining, to empower training designers to delve into datasets derived from cyber security training exercises.

DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles.

Transcription regulation in multicellular species is mediated by modular transcription factor (TF) binding site combinations termed cis-regulatory modules (CRMs). Such CRM-mediated transcription regulation determines the gene expression patterns during development. Biologists frequently investigate CRM transcription regulation on gene expressions. However, the knowledge of the target genes and regulatory TFs participating in the CRMs under study is mostly fragmentary throughout the literature. Researchers need to afford tremendous human resources to fully surf through the articles deposited in biomedical literature databases in order to obtain the information. Although several novel text-mining systems are now available for literature triaging, these tools do not specifically focus on CRM-related literature prescreening, failing to correctly extract the information of the CRM target genes and regulatory TFs from the literature. For this reason, we constructed a supportive auto-literature prescreener called Drosophila Modular transcription-regulation Literature Screener (DMLS) that achieves the following: (i) prescreens articles describing experiments on modular transcription regulation, (ii) identifies the described target genes and TFs of the CRMs under study for each modular transcription-regulation-describing article and (iii) features an automated and extendable pipeline to perform the task. We demonstrated that the final performance of DMLS in extracting the described target gene and regulatory TF lists of CRMs under study for given articles achieved test macro area under the ROC curve (auROC) = 89.7% and area under the precision-recall curve (auPRC) = 77.6%, outperforming the intuitive gene name-occurrence-counting method by at least 19.9% in auROC and 30.5% in auPRC. The web service and the command line versions of DMLS are available at https://cobis.bme.ncku.edu.tw/DMLS/ and https://github.com/cobisLab/DMLS/, respectively. Database Tool URL: https://cobis.bme.ncku.edu.tw/DMLS/.

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding.

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

ExonSurfer: a web-tool to design primers at exon–exon junctions

BackgroundReverse transcription quantitative PCR (RT-qPCR) with intercalating dyes is one of the main techniques to assess gene expression levels used in basic and applied research as well as in diagnostics. However, primer design for RT-qPCR can be complex due to the high demands on primer quality. Primers are best placed on exon junctions, should avoid polymorphic regions, be specific to the target transcripts and also prevent genomic amplification accurately, among others. Current software tools manage to meet all the necessary criteria only insufficiently. Here, we present ExonSurfer, a novel, user-friendly web-tool for qPCR primer design.ResultsExonSurfer combines the different steps of the primer design process, encompassing target selection, specificity and self-complementarity assessment, and the avoidance of issues arising from polymorphisms. Amplification of potentially contaminating genomic DNA is avoided by designing primers on exon-exon junctions, moreover, a genomic alignment is performed to filter the primers accordingly and inform the user of any predicted interaction. In order to test the whole performance of the application, we designed primer pairs for 26 targets and checked both primer efficiency, amplicon melting temperature and length and confirmed the targeted amplicon by Sanger sequencing. Most of the tested primers accurately and selectively amplified the corresponding targets.ConclusionExonSurfer offers a comprehensive end-to-end primer design, guaranteeing transcript-specific amplification. The user interface is intuitive, providing essential specificity and amplicon details. The tool can also be used by command line and the source code is available. Overall, we expect ExonSurfer to facilitate RT-qPCR set-up for researchers in many fields.

EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions

Abstract. The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.

Bigtools: a high-performance BigWig and BigBed library in Rust.

The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications. Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python. Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io, Bioconda, and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

MetaAnalysisOnline.com: A new easy-to-use bioinformatic tool for the meta-analysis of clinical trials.

e23012 Background: A meta-analysis is a formal, quantitative study design in epidemiology and clinical medicine that systematically assesses and consolidates previous research findings to derive comprehensive conclusions on a specific topic. Here, we aimed to establish a new tool for the visualization options useful for conducting a meta-analysis specifically designed for oncology trials. Methods: A meta-analysis can be based on binary data, continuous data, or time-to-event data. The most crucial statistical models, including the random effect model and the fixed effect model, are outlined alongside their respective statistical methods. Furthermore, we implemented graphical representations such as the forest plot, funnel plots, and Z-scope plots. While the forest plot effectively illustrates heterogeneity and pooled results, a funnel plot can reveal potential publication bias, and a Z-score plot shows the robustness of the used sample number. Results: The models and visualization options outlined have been integrated into a new, online web portal which can be accessed without the need for registration. The web application operates on an Ubuntu server running Apache and enables users to conduct meta-analyses using results typically used in oncology trials including binary (total and event numbers), continuous (mean and standard deviation data), and time-to-event data (hazard rate and CI data). Study results from common spreadsheet applications like Excel can be directly input into the system. The online platform utilizes the meta and metafor packages in the R programming environment (R version 4.2.2) for calculations and plots. The Shiny user interface is powered by shinyjs, shinydashboard, and rhandsontable R packages. The portal generates a forest plot to summarize meta-analysis results, a funnel plot for visual detection of potential bias, and a Z-score plot showing the sufficiency of the cumulative sample number. The analysis results of the tool were validated using IBM SPSS v28. The new bioinformatic tool is available at www.metaanalysisonline.com and requires no programming knowledge or command line use. Conclusions: Our platform aims to provide a user-friendly meta-analysis tool for epidemiological studies and oncology clinical trials, offering a swift and reproducible way to integrate the results from multiple studies.

PyHySCO: GPU-enabled susceptibility artifact distortion correction in seconds.

Over the past decade, reversed gradient polarity (RGP) methods have become a popular approach for correcting susceptibility artifacts in echo-planar imaging (EPI). Although several post-processing tools for RGP are available, their implementations do not fully leverage recent hardware, algorithmic, and computational advances, leading to correction times of several minutes per image volume. To enable 3D RGP correction in seconds, we introduce PyTorch Hyperelastic Susceptibility Correction (PyHySCO), a user-friendly EPI distortion correction tool implemented in PyTorch that enables multi-threading and efficient use of graphics processing units (GPUs). PyHySCO uses a time-tested physical distortion model and mathematical formulation and is, therefore, reliable without training. An algorithmic improvement in PyHySCO is its use of the one-dimensional distortion correction method by Chang and Fitzpatrick to initialize the non-linear optimization. PyHySCO is published under the GNU public license and can be used from the command line or its Python interface. Our extensive numerical validation using 3T and 7T data from the Human Connectome Project suggests that PyHySCO can achieve accuracy comparable to that of leading RGP tools at a fraction of the cost. We also validate the new initialization scheme, compare different optimization algorithms, and test the algorithm on different hardware and arithmetic precisions.

Development and application of EpitopeScan, a Python3 toolset for mutation tracking in SARS-CoV-2 immunogenic epitopes.

Outbreaks of coronaviruses and especially the recent COVID-19 pandemic emphasize the importance of immunological research in this area to mitigate the effect of future incidents. Bioinformatics approaches are capable of providing multisided insights from virus sequencing data, although currently available software options are not entirely suitable for a specific task of mutation surveillance within immunogenic epitopes of SARS-CoV-2. Here, we describe the development of a mutation tracker, EpitopeScan, a Python3 package with command line and graphical user interface tools facilitating the investigation of the mutation dynamics in SARS-CoV-2 epitopes via analysis of multiple-sequence alignments of genomes over time. We provide an application case by examining three Spike protein-derived immunodominant CD4+ T-cell epitopes restricted by HLA-DRB1*04:01, an allele strongly associated with susceptibility to rheumatoid arthritis (RA). Mutations in these peptides are relevant for immune monitoring of CD4+ T-cell responses against SARS-CoV-2 spike protein in patients with RA. The analysis focused on 2.3 million SARS-CoV-2 genomes sampled in England. We detail cases of epitope conservation over time, partial loss of conservation, and complete divergence from the wild type following the emergence of the N969K Omicron-specific mutation in November 2021. The wild type and the mutated peptide represent potential candidates to monitor variant-specific CD4+ T-cell responses. EpitopeScan is available via GitHub repository https://github.com/Aleksandr-biochem/EpitopeScan.

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz.

Enhancing MD simulations: ASGARD's automated analysis for GROMACS.

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.

Prediction of rolling resistance and wheel force for a passenger car tire: A comparative study on the use of different material models and numerical approaches

In this research, the characteristics of tire-road interaction of a 185/65R14 88H passenger car tire are investigated using the Finite Element Method in Abaqus commercial software. Moreover, the effect of various material models on tire performance is studied by implementing Visco-Hyperelastic, Parallel Rheological Framework, and Mullins effect. The novelty of this research is devoted to the development of the complex material models particularly considering the Mullins effect of the rubber compounds in the tire structure for the load-displacement criteria. For this purpose, a tire finite element model was generated using Abaqus/Standard command line in two different methods including an Arbitrary Lagrangian-Eulerian formulation for steady state rolling and implementing a pure Lagrangian approach for the transient dynamic analysis carried out implicit and explicit process respectively. Rolling resistance force was computed according to ISO 28580 with 210 kPa inflation pressure and 4155 N vertical load. The footprint test results were extracted in both static and transient dynamic analyses. Additionally, the wheel reaction force was predicted using an indirect method by extracting the tire-terrain contact patch reaction force in Abaqus/Explicit to observe the effect of the material convection along with stress softening phenomena of the rubber compounds of tire structure. In the post-processing analysis, the wheel reaction was filtered by implementing SAE60 filter to reduce the numerical noise in the final response.

Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

BackgroundAs genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype.ResultsIn this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation.ConclusionsGaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

Background and ObjectiveA new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. MethodsMMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. ResultsAs an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. ConclusionsThanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Cooltools: Enabling high-resolution Hi-C analysis in Python.

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.