Docker Image Research Articles

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10–15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

A Raspberry Pi based blockchain application on IoT security

Internet of Things (IoT) is a future generation of Internet infrastructure comprised of quick-witted things outfitted with programs and electric eyes or sensors that are engaged in an extent of industries such as media, robotics, government, healthcare, and most notably, education. It is composed of connected devices equipped with software and sensors that are able to communicate with each other and facilitate automated operations. This next generation of Internet infrastructure is set to bring about unprecedented levels of efficiency and convenience. In this day and age, these industries are utilizing Quick Response (QR) codes for daily transactions due to their straightforwardness and expediency. The utilization of this technology can be beneficial for students, as many people tend to overlook the importance of attendance in daily lives. Nevertheless, attendance is essential for both students and instructors, as it illustrates who is present in the classroom. The goal of this study is to gain or provide a systematized, reliable, and secure data interchange on IoT for education particularly for students. In the realm of computing, arm64 and amd64 are both examples of 64-bit processor architectures; arm64 is smaller and uses less energy, while amd64 is bigger and more powerful. Though they may differ in their designs and implementations, both processors can achieve the same results when used to develop an attendance system with blockchain technology. This study constitutes a straightforward and efficient system application for students that victoriously run proficiently with the constructive help of QR code, raspberry pi, and also the Hyperledger Fabric blockchain which can support to increase the security of the system. This study successfully builds a newly update Fabric 2.4.7 docker images and binaries for the arm64, modifies the HLF transaction and adds a Secure Hash Algorithm or the SHA256 generated by crypto from NodeJS of each transaction key and creates a QR code from it, includes a student transaction time for both attendance and registration.

Asterics: a simple tool for the ExploRation and Integration of omiCS data

BackgroundThe rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets.ResultsASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result.ConclusionASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available at https://asterics.miat.inrae.fr and can also be deployed using provided docker images.

AlexandrusPS: A User-Friendly Pipeline for the Automated Detection of Orthologous Gene Clusters and Subsequent Positive Selection Analysis.

The detection of adaptive selection in a system approach considering all protein-coding genes allows for the identification of mechanisms and pathways that enabled adaptation to different environments. Currently, available programs for the estimation of positive selection signals can be divided into two groups. They are either easy to apply but can analyze only one gene family at a time, restricting system analysis; or they can handle larger cohorts of gene families, but require considerable prerequisite data such as orthology associations, codon alignments, phylogenetic trees, and proper configuration files. All these steps require extensive computational expertise, restricting this endeavor to specialists. Here, we introduce AlexandrusPS, a high-throughput pipeline that overcomes technical challenges when conducting transcriptome-wide positive selection analyses on large sets of nucleotide and protein sequences. The pipeline streamlines 1) the execution of an accurate orthology prediction as a precondition for positive selection analysis, 2) preparing and organizing configuration files for CodeML, 3) performing positive selection analysis using CodeML, and 4) generating an output that is easy to interpret, including all maximum likelihood and log-likelihood test results. The only input needed from the user is the CDS and peptide FASTA files of proteins of interest. The pipeline is provided in a Docker image, requiring no program or module installation, enabling the application of the pipeline in any computing environment. AlexandrusPS and its documentation are available via GitHub (https://github.com/alejocn5/AlexandrusPS).

Implementation of Continous Delivery using Jenkins And Kubernetes with Docker Local Images

The need for software has increased and the development process has also become more complex as technology has developed which resulted application will take a long time to deployed, which can be completed in weeks or even months. One reason is the large number of teams involved in application development, especially the Development Team and Operations Team. These teams can cause the application delivery process to the user's side to be hampered if a conflict occurs, so the term DevOps appears. Support for DevOps continues to be improved, so there is CI/CD (Continuous Integration and Continuous Delivery/Deployment). Banyak penelitian mengenai CI/CD yang sudah dilakukan dan menggunakan tools Jenkins, Kubernetes, dan Docker. Namun penelitian yang sudah ada menggunakan repository DockerHub untuk menyimpan Docker Image This research focuses on the local implementation of the docker image which is then run with Kubernetes Orchestration so it can reduce the platform used by CI/CD. This implementation requires conversion from docker image to Kubernetes Image Cache. The results show that the Continous Delivery implementation using Kubernetes with the Local Docker image is successful and can run well. The results show that the average time required to create a docker image is 649 seconds (10 minutes 49 seconds) and image conversion process which takes an average time of 275 seconds (4 minutes 35 seconds). Research can be developed further by researching techniques to shorten build time, minimize resource utilitation and minimize time conversion from docker images to Kubernetes Image Cache.

What Quality Aspects Influence the Adoption of Docker Images?

Docker is a containerization technology that allows developers to ship software applications along with their dependencies in Docker images. Developers can extend existing images using them as base images when writing Dockerfiles. However, a lot of alternative functionally equivalent base images are available. Although many studies define and evaluate quality features that can be extracted from Docker artifacts, the criteria on which developers choose a base image over another remain unclear. In this article, we aim to fill this gap. First, we conduct a literature review through which we define a taxonomy of quality features, identifying two main groups: configuration-related features (i.e., mainly related to the Dockerfile and image build process), and externally observable features (i.e., what the Docker image users can observe). Second, we ran an empirical study considering the developers’ preference for 2,441 Docker images in 1,911 open source software projects. We want to understand how the externally observable features influence the developers’ preferences, and how they are related to the configuration-related features. Our results pave the way to the definition of a reliable quality measure for Docker artifacts, along with tools that support developers for a quality-aware development of them.

COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.

Evaluation of synthetically generated computed tomography for use in transcranial focused ultrasound procedures.

Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under magnetic resonance imaging (MRI) guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful radiation. A method to estimate acoustic parameters in the skull without the need for CT is desirable. We synthesized CT images from routinely acquired T1-weighted MRI using a 3D patch-based conditional generative adversarial network and evaluated the performance of synthesized CT (sCT) images for treatment planning with tFUS. We compared the performance of sCT with real CT (rCT) images for tFUS planning using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios: (1)no aberration correction, (2)correction with phases calculated from Kranion, and (3)phase shifts calculated from time reversal. From Kranion, the skull density ratio, skull thickness, and number of active elements between rCT and sCT had Pearson's correlation coefficients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction () and smallest with Kranion phases (). The distance between peak focal locations between rCT and sCT was for all simulation cases. Real and synthetically generated skulls had comparable image similarity, skull measurements, and acoustic simulation metrics. Our work demonstrated similar results for 10 testing cases comparing MR-sCTs and rCTs for tFUS planning. Source code and a docker image with the trained model are available at https://github.com/han-liu/SynCT_TcMRgFUS.

EnsemblQueryR: fast, flexible and high-throughput querying of Ensembl LD API endpoints in R.

We present ensemblQueryR, an Rpackage for querying Ensembl linkage disequilibrium (LD) endpoints. This package is flexible, fast and user-friendly, and optimised for high-throughput querying. ensemblQueryR uses functions that are intuitive and amenable to custom code integration, familiar R object types as inputs and outputs as well as providing parallelisation functionality. For each Ensembl LD endpoint, ensemblQueryR provides two functions, permitting both single- and multi-query modes of operation. The multi-query functions are optimised for large query sizes and provide optional parallelisation to leverage available computational resources and minimise processing time. We demonstrate improved computational performance of ensemblQueryR over an exisiting tool in terms of random access memory (RAM) usage and speed, delivering a 10-fold speed increase whilst using a third of the RAM. Finally, ensemblQueryR is near-agnostic to operating system and computational architecture through Docker and singularity images, making this tool widely accessible to the scientific community.

Galba: genome annotation with miniprot and AUGUSTUS

BackgroundThe Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes.ResultsVarious gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments.ConclusionsOur pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

Reproducible experiments with Learned Metric Index Framework

This work is a companion reproducible paper of a previous paper (Antol et al., 2021) in which we presented an alternative to the traditional paradigm of similarity searching in metric spaces called the Learned Metric Index. Inspired by the advance in learned indexing of structured data, we used machine learning models to replace index pivots, thus posing similarity search as a classification problem. This implementation proved to be more than competitive with the conventional methods in terms of speed and recall, proving the concept as viable. The aim of this publication is to make our source code, datasets, and experiments publicly available. For this purpose, we create a collection of Python3 software libraries, YAML reproducible experiment files, and JSON ground-truth files, all bundled in a Docker image – the Learned Metric Index Framework (LMIF) – which can be run using any Docker-compatible operating system on a CPU with Advanced vector extensions (AVX). We introduce a reproducibility protocol for our experiments using LMIF and provide a closer look at the experimental process. We introduce new experimental results by running the reproducibility protocol introduced herein and discussing the differences with the results reported in our primary work (Antol et al., 2021). Finally, we make an argument that these results can be considered weakly reproducible (in both of the performance metrics), since they point to the same conclusions derived in the primary paper.

DREAM: an R package for druggability evaluation of human complex diseases.

De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles

In transportation, a vehicle's route is one of the most private information. However, to mutually learn some phenomena in a city, for example, parking lot occupancies, we might have to reveal information about it. In this paper, we focus on assessing the privacy loss in a vehicular federated machine learning system. For the analysis, we used the Monaco SUMO Traffic Scenario (MoST). We also used the simulation inputs as statistical data to calculate privacy loss metrics. Results show that a vehicular federated machine learning system may pose a smaller privacy threat than individual learning, but its performance is lower compared to a centralized learning approach.
 Due to the vast amount of data and processing time, we also describe a method to build a Docker image of SUMO together with a software client-server architecture for SUMO-based learning systems on multiple computers.

Confine: Fine-grained system call filtering for container attack surface reduction

Reducing the attack surface of the OS kernel is a promising defense-in-depth approach for mitigating the fragile isolation guarantees of container environments. In contrast to hypervisor-based systems, malicious containers can exploit vulnerabilities in the underlying kernel to fully compromise the host and all other containers running on it. Previous container attack surface reduction efforts have relied on dynamic analysis and training using representative workloads to limit the set of system calls exposed to containers. These approaches, however, do not capture exhaustively all the code that can potentially be needed by future workloads or rare runtime conditions, and are thus not appropriate as a generic solution.Aiming to provide a practical solution for the protection of arbitrary containers, in this paper we present a generic approach for the automated generation of restrictive system call policies for Docker containers. Our system, named Confine, uses static code analysis to inspect the containerized application and all its dependencies, identify the superset of system calls required for the correct operation of the container, and generate both a container-wide and application-specific Seccomp system call policy that can be readily enforced while loading the container and launching the main program. We also show that further attack surface reduction is possible by enforcing fine-grained system call policies that do not only consider the system calls used by the target application, but also their argument values.The results of our experimental evaluation with a set of 27 Docker images show that applying container-wide filtering disables more than 145 system calls on average across the entire container, and application-specific filtering increases the number of filtered system calls by 25% on average, as many system calls used exclusively by utilities and scripts during the container’s initialization phase can be safely removed afterwards.

Implementation of a Continuous Integration and Deployment Pipeline for Containerized Applications in Amazon Web Services Using Jenkins

This project focuses on streamlining the software development process by integrating various tools and automating key tasks. Developers contribute code changes to a Git repository hosted on GitHub. These changes trigger Jenkins, a continuous integration and delivery tool, to build the code using Maven, a build automation tool. Maven compiles the code and executes unit tests to ensure its correctness. To access code quality, security vulnerabilities, and bugs, sonarqube, a static code analysis tool, is employed. As part of the automation process, the built code is packaged into a Docker image. This image is then automatically pushed to dockerhub, a container registry, with a new version identifier. Additionally, a shell script is utilized to update the manifest deployment file, which specifies the version and configuration details for deployment, with the newly created version. This ensures that the deployment process is seamlessly synchronized with the latest code changes. By integrating these tools and automating the necessary steps, this project enhances the efficiency and reliability of the software development pipeline. It facilitates rapid code testing, quality analysis, and version management, thereby improving the overall development workflow.

A Web-Based Docker Image Assistant Generation Tool for User-PC Computing System

Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle every necessary software as one image file. Unfortunately, the Docker file/image are manually generated through multiple steps by a user, which can be the bottleneck. In this paper, we present a web-based Docker image assistant generation (DIAG) tool in the UPC system to assist or reduce these process steps. It adopts Angular JavaScript for offering user interfaces, PHP Laravel for handling logic using RestAPI, MySQL database for storing data, and Shell scripting for speedily running the whole program. In addition, the worker-side code modification function is implemented so that a user can modify the source code of the running job and update the Docker image at a worker to speed up them. For evaluations, we collected 30 Docker files and 10 OpenFOAM jobs through reverse processing from Docker images in Github and generated the Docker images using the tool. Moreover, we modified source codes for network simulations and generated the Docker images in a worker five times. The results confirmed the validity of the proposal.

PHASTEST: faster than PHASTER, better than PHAST.

PHASTEST (PHAge Search Tool with Enhanced Sequence Translation) is the successor to the PHAST and PHASTER prophage finding web servers. PHASTEST is designed to support the rapid identification, annotation and visualization of prophage sequences within bacterial genomes and plasmids. PHASTEST also supports rapid annotation and interactive visualization of all other genes (protein coding regions, tRNA/tmRNA/rRNA sequences) in bacterial genomes. Given that bacterial genome sequencing has become so routine, the need for fast tools to comprehensively annotate bacterial genomes has become progressively more important. PHASTEST not only offers faster and more accurate prophage annotations than its predecessors, it also provides more complete whole genome annotations and much improved genome visualization capabilities. In standardized tests, we found that PHASTEST is 31% faster and 2-3% more accurate in prophage identification than PHASTER. Specifically, PHASTEST can process a typical bacterial genome in 3.2 min(raw sequence) or in 1.3 minwhen given a pre-annotated GenBank file. Improvements in PHASTEST's ability to annotate bacterial genomes now make it a particularly powerful tool for whole genome annotation. In addition, PHASTEST now offers a much more modern and responsive visualization interface that allows users to generate, edit, annotate and interactively visualize (via zooming, rotating, dragging, panning, resetting), colourful, publication quality genome maps. PHASTEST continues to offer popular options such as an API for programmatic queries, a Docker image for local installations, support for multiple (metagenomic) queries and the ability to perform automated look-ups against thousands of previously PHAST-annotated bacterial genomes. PHASTEST is available online at https://phastest.ca.

ChroKit: a Shiny-based framework for interactive analysis, visualization and integration of genomic data.

We developed ChroKit (the Chromatin toolKit), an interactive web-based framework written in R that enables intuitive exploration, multidimensional analyses, and visualization of genomic data from ChIP-Seq, DNAse-Seq or any other NGS experiment that reports the enrichment of aligned reads over genomic regions. This program takes preprocessed NGS data and performs operations on genomic regions of interest, including resetting their boundaries, their annotation based on proximity to genomic features, the association to gene ontologies, and signal enrichment calculations. Genomic regions can be further refined or subsetted by user-defined logical operations and unsupervised classification algorithms. ChroKit generates a full range of plots that are easily manipulated by point and click operations, thus allowing 'on the fly' re-analysis and fast exploration of the data. Working sessions can be exported for reproducibility, accountability, and easy sharing within the bioinformatics community. ChroKit is multiplatform and can be deployed on a server to enhance computational speed and provide simultaneous access by multiple users. ChroKit is a fast and intuitive genomic analysis tool suited for a wide range of users due to its architecture and its user-friendly graphical interface. ChroKit source code is available at https://github.com/ocroci/ChroKit and the Docker image at https://hub.docker.com/r/ocroci/chrokit.

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters

BackgroundCo-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret.ResultsThe comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query.ConclusionOverall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl.

Automating microbial taxonomy workflows with PHANTASM: phylogenomic analyses for the taxonomy and systematics of microbes.

The nucleotide sequences of 16S ribosomal RNA (rRNA) genes have been used to inform the taxonomic placement of prokaryotes for several decades. Whole-genome approaches can better resolve evolutionary relationships of organisms, but these analyses often require computational proficiencies that are uncommon among microbiologists. PHANTASM is a new tool capable of automating these workflows. This tool was designed to work for a wide range of prokaryotes and is the first example of an automated reconciliation of NCBI's Taxonomy database with that of the List of Prokaryotic names with Standing in Nomenclature (LPSN). In this study, we describe the workflow of PHANTASM and provide several examples of results generated by it. The source code is freely-available on GitHub. In order to facilitate the ease-of-access for researchers, PHANTASM is also available as a Docker image. While other tools exist to facilitate starting points for these analyses, PHANTASM provides users with a greater degree of control and produces outputs that can be used to make publication-quality figures.