Docker Hub Research Articles

LmRaC: a functionally extensible tool for LLM interrogation of user experimental results.

Large Language Models (LLMs) have provided spectacular results across a wide variety of domains. However, persistent concerns about hallucination and fabrication of authoritative sources raise serious issues for their integral use in scientific research. Retrieval-augmented generation (RAG) is a technique for making data and documents, otherwise unavailable during training, available to the LLM for reasoning tasks. In addition to making dynamic and quantitative data available to the LLM, RAG provides the means by which to carefully control and trace source material, thereby ensuring results are accurate, complete and authoritative. Here we introduce LmRaC, an LLM-based tool capable of answering complex scientific questions in the context of a user's own experimental results. LmRaC allows users to dynamically build domain specific knowledge-bases from PubMed sources (RAGdom). Answers are drawn solely from this RAG with citations to the paragraph level, virtually eliminating any chance of hallucination or fabrication. These answers can then be used to construct an experimental context (RAGexp) that, along with user supplied documents (e.g., design, protocols) and quantitative results, can be used to answer questions about the user's specific experiment. Questions about quantitative experimental data are integral to LmRaC and are supported by a user-defined and functionally extensible REST API server (RAGfun). Detailed documentation for LmRaC along with a sample REST API server for defining user functions can be found at https://github.com/dbcraig/LmRaC. The LmRaC web application image can be pulled from Docker Hub (https://hub.docker.com) as dbcraig/lmrac.

OligoFormer: an accurate and robust prediction method for siRNA design.

RNA interference (RNAi) has become a widely used experimental approach for post-transcriptional regulation and is increasingly showing its potential as future targeted drugs. However, the prediction of highly efficient siRNAs (small interfering RNAs) is still hindered by dataset biases, the inadequacy of prediction methods, and the presence of off-target effects. To overcome these limitations, we propose an accurate and robust prediction method, OligoFormer, for siRNA design. OligoFormer comprises three different modules including thermodynamic calculation, RNA-FM module, and Oligo encoder. Oligo encoder is the core module based on the transformer encoder. Taking siRNA and mRNA sequences as input, OligoFormer can obtain thermodynamic parameters, RNA-FM embedding, and Oligo embedding through these three modules, respectively. We carefully benchmarked OligoFormer against six comparable methods on siRNA efficacy datasets. OligoFormer outperforms all the other methods, with an average improvement of 9% in AUC, 6.6% in PRC, 9.8% in F1 score, and 5.1% in PCC compared to the best method among them in our inter-dataset validation. We also provide a comprehensive pipeline with prediction of siRNA efficacy and off-target effects using PITA score and TargetScan score. The ablation study shows RNA-FM module and thermodynamic parameters improved the performance and accelerated convergence of OligoFormer. The saliency maps by gradient backpropagation and base preference maps show certain base preferences in initial and terminal region of siRNAs. The source code of OligoFormer is freely available on GitHub at: https://github.com/lulab/OligoFormer. Docker image of OligoFormer is freely available on the docker hub at https://hub.docker.com/r/yilanbai/oligoformer.

Inferences on the evolution of the ascorbic acid synthesis pathway in insects using Phylogenetic Tree Collapser (PTC), a tool for the automated collapsing of phylogenetic trees using taxonomic information.

When inferring the evolution of a gene/gene family, it is advisable to use all available coding sequences (CDS) from as many species genomes as possible in order to infer and date all gene duplications and losses. Nowadays, this means using hundreds or even thousands of CDSs, which makes the inferred phylogenetic trees difficult to visualize and interpret. Therefore, it is useful to have an automated way of collapsing large phylogenetic trees according to a taxonomic term decided by the user (family, class, or order, for instance), in order to highlight the minimal set of sequences that should be used to recapitulate the full history of the gene/gene family being studied at that taxonomic level, that can be refined using additional software. Here we present the Phylogenetic Tree Collapser (PTC) program (https://github.com/pegi3s/phylogenetic-tree-collapser), a flexible tool for automated tree collapsing using taxonomic information, that can be easily used by researchers without a background in informatics, since it only requires the installation of Docker, Podman or Singularity. The utility of PTC is demonstrated by addressing the evolution of the ascorbic acid synthesis pathway in insects. A Docker image is available at Docker Hub (https://hub.docker.com/r/pegi3s/phylogenetic-tree-collapser) with PTC installed and ready-to-run.

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BackgroundFungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsHere we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.ConclusionsHiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac.

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .

An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users.

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BackgroundDetection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.ResultsWe have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.ConclusionsPhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe.

On the Security of Containers: Threat Modeling, Attack Analysis, and Mitigation Strategies

Traditionally, applications that are used in large and small enterprises were deployed on “bare metal” servers installed with operating systems. Recently, the use of multiple virtual machines (VMs) on the same physical server was adopted due to cost reduction and flexibility. Nowadays, containers have become popular for application deployment due to smaller footprints than the VMs, their ability to start and stop more quickly, and their capability to pack the application binaries and their dependencies/libraries in standalone units for seamless portability. A typical container ecosystem includes a code repository (e.g., GitHub) where the container images are built from the codes and libraries and then pushed to the image registry (e.g., Docker Hub) for subsequent deployment as application containers. However, the pervasive use of containers also leads to a wide-range of security breaches such as attackers stealing credentials, source codes and sensitive data from image registry and code repository, carrying out DoS attacks on application containers, and gaining root access to misuse the underlying host resources, among others. In this paper, we first perform threat modeling on the containers ecosystem using the popular threat modeling framework, called STRIDE. Using STRIDE, we identify the vulnerabilities in each system component, and investigate potential security threats and their consequences. Then, we conduct a comprehensive survey on the existing countermeasures designed against the identified threats and vulnerabilities in containers. In particular, we assess the strengths and weaknesses of the existing mitigation strategies designed against such threats. We believe that this work will help researchers and practitioners to gain a deeper understanding of the threat landscape in containers and the state-of-the-art countermeasures. We also discuss open research problems, the research gaps and future research directions in containers security, which may ignite further research to be done in this area.

Cryptojacking Malware Detection in Docker Images Using Supervised Machine Learning

Nowadays, Docker Containers are currently being adopted as industry standards for software delivery, because they provide quick and responsive delivery and handle performance and scalability challenges. However, attackers are exploiting them to introduce malicious instructions in publicly available images to perform unauthorized use of third-party’s computer resources for Cryptojacking. We developed a machine learning based model to detect Docker images that lead to cryptojacking. The dataset used is composed of 800 Docker images collected from Docker hub, half of which contains instructions for cryptomining, and the other half does not contain such instructions. We trained 10 classification algorithms and evaluated them using the K-Fold Cross Validation approach. The results showed accuracy scores ranging from 89% to 97%. Stochastic Gradient Descent for Logistic Regression outperformed the other algorithms reaching an accuracy score of 97%. With these results, we conclude that machine learning algorithms can detect Docker images carrying cryptojacking malware with a good performance.

A Lightweight Portable Multithreaded Client-Server Docker Containers

In terms of diversity in operating systems, environments, and platforms, and with limited host infrastructure resources to hold all operating systems and platforms, the need arises to design applications that run in many or we can say in all operating systems and platforms. This paper deals with designing and implementing a lightweight multithreaded client-server application appears as a separate Docker container. Both client and server containers are based on alpine Linux and developed using Python programming language. The execution unit in the containers is Python program files. As a case study, the server acts as a Wikipedia server and it can serve many clients simultaneously. The Docker will build the containers depending on a writing Dockerfile for each container and push them to the registry (docker hub). When pulled the image of the containers from the registry account, then it could be run the container on a host. The proposed containerized multithreaded client-server model will become a portable with limited capabilities other than using Virtualization. Resources and cost requirements to achieve portability is evaluated for both virtualization and containerization paradigms. The results showed the superiority of containerization over the virtualization in both resources and cost requirements.

SEAF: A Scalable, Efficient, and Application-independent Framework for container security detection

Container technology has become a popular development that can conveniently accelerate building, running, and sharing applications. However, a container image packaging a collection of software usually lurks various defects threatening consumer safety, such as embedded malware, software vulnerability, privacy leakage, etc. Moreover, developers and users share container images through a centralized, public, and massive repository (e.g., Docker Hub), which can magnify the impact of these security defects in a fast-spreading way. Unfortunately, existing detection methods cannot effectively or efficiently discover such hidden flaws among the numerous images.This paper proposes a novel method to effectively detect and measure container security flaws embedded in images. Based on the crucial insight that container images are constructed hierarchically, each image depends on layers of forwarding image and adds updated content in layers of itself. Our work mines a Global Relationship Tree (GRT) based on dependency among the images that contain common layers. Meanwhile, by traversing the GRT and leveraging content differential analysis, we can locate the changing content in an image corresponding to defects. Therefore, when checking flaws among numerous images, we make a layer-sensitive detection by reusing common layers’ detection results in iterative processes to boost detection and accurately measure the influence scope of defects. Finally, we summarize and develop a set of detection primitives for scaling our approach to handle various flaws that may lead to multiple risks in potential.Depending upon this method, we implemented SEAF, a Scalable, Efficient, and Application-independent Framework, and evaluated it on popular images of diverse applications in Docker Hub. The experiment result shows that SEAF can discover different security flaws fast. Compared to the state-of-the-art tool, Clair, SEAF is more efficient and can find significantly more types of defects.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

WAT3R: recovery of T-cell receptor variable regions from 3' single-cell RNA-sequencing.

Diversity of the T-cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and β chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here, we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T-cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T-cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T-cell types, indicating an association between T-cell clonotypes and differentiation states. The Workflow for Association of T-cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r) and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956). Supplementary data are available at Bioinformatics online.

MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks

Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).

WGA-LP: a pipeline for whole genome assembly of contaminated reads.

Whole genome assembly (WGA) of bacterial genomes with short reads is a quite common task as DNA sequencing has become cheaper with the advances of its technology. The process of assembling a genome has no absolute golden standard and it requires to perform a sequence of steps each of which can involve combinations of many different tools. However, the quality of the final assembly is always strongly related to the quality of the input data. With this in mind we built WGA-LP, a package that connects state-of-the-art programs for microbial analysis and novel scripts to check and improve the quality of both samples and resulting assemblies. WGA-LP, with its conservative decontamination approach, has shown to be capable of creating high quality assemblies even in the case of contaminated reads. WGA-LP is available on GitHub (https://github.com/redsnic/WGA-LP) and Docker Hub (https://hub.docker.com/r/redsnic/wgalp). The web app for node visualization is hosted by shinyapps.io (https://redsnic.shinyapps.io/ContigCoverageVisualizer/). Supplementary data are available at Bioinformatics online.

ElectroMicroTransport v2107: Open-source toolbox for paper-based electromigrative separations

electroMicroTransport v2107: Open-source toolbox for paper-based electromigrative separations

On the usage of JavaScript, Python and Ruby packages in Docker Hub images

Docker is one of the most popular containerization technologies. A Docker container can be saved into an image including all environmental packages required to run it, such as system and third-party packages from language-specific package repositories. Relying on its modularity, an image can be shared and included in other images to simplify the way of building and packaging new software. However, some package managers allow to include duplicated packages in an image, increasing its footprint; and outdated packages may miss new features and bug fixes or contain reported security vulnerabilities, putting the image in which they are contained at risk. Previous research has focused on studying operating system packages within Docker images, but little attention has been given to third-party packages. This article empirically studies installation practices, outdatedness and vulnerabilities of JavaScript, Python and Ruby packages installed in 3,000 popular community Docker Hub images. In many cases, these installed packages missed important releases leading to potential vulnerabilities of the images. Our findings suggest that maintainers of Docker Hub community images should invest more effort in updating outdated packages contained in those images in order to significantly reduce the number of vulnerabilities. In addition to this, Python community images are generally much less outdated and much less subject to vulnerabilities than NodeJS and Ruby community images. Specifically for NodeJS community images, elimination of duplicate package releases could lead to a significant reduction in their image footprint.

Implementation of Docker for mobile edge computing embedded platform

Some of the glitches in Mobile cloud computing are high latency, network bottleneck, and congestion. From a centralized to a decentralized computing scenario, Mobile Edge computing can transfer the computation to the edge, which decreases the response time for the application and increase the user experience. This paper assesses Docker as a medium for Edge Computing, a container-based technology. Docker can be seen as an open platform for designers and system managers to use Docker Engine, a portable, lightweight runtime and packaging tool to build, ship, run applications, and Docker hub. This cloud service distributes applications and mechanizing workflows. The key benefit is that, as soon as possible, Docker can get code checked and can fit into production. This paper will discuss Docker and present how this technology has overcome the earlier problems of virtualization with building and deploying large applications in two ways. We have implemented Docker of YOLO and AQM models on Jetson TX2 and compared both applications on Docker and Host OS. We can use dockers in two ways either we can use previously made dockers or build our own Docker according to our needs. In this paper, I have used the Docker image of YOLO and built my own Docker for AQM Model. These Docker containers' performance measured in this paper based on their device performance, memory consumption, boot time, and Docker performed very well.

A multi-dimensional analysis of technical lag in Debian-based Docker images

Container-based solutions, such as Docker, have become increasingly relevant in the software industry to facilitate deploying and maintaining software systems. Little is known, however, about how outdated such containers are at the moment of their release or when used in production. This article addresses this question, by measuring and comparing five different dimensions of technical lag that Docker container images can face: package lag, time lag, version lag, vulnerability lag, and bug lag. We instantiate the formal technical lag framework from previous work to operationalise these different dimensions of lag on Docker Hub images based on the Debian Linux distribution. We carry out a large-scale empirical study of such technical lag, over a three-year period, in 140,498 Debian images. We compare the differences between official and community images, as well as between images with different Debian distributions: OldStable, Stable or Testing. The analysis shows that the different dimensions of technical lag are complementary, providing multiple insights. Official Debian images consistently have a lower lag than community images for all considered lag dimensions. The amount of lag incurred depends on the type of Debian distribution and the considered lag dimension. Our research offers empirical evidence that developers and deployers of Docker images can benefit from identifying to which extent their containers are outdated according to the considered dimensions, and mitigate the risks related to such outdatedness.

ConPlot: web-based application for the visualization of protein contact maps integrated with other data.

SummaryCovariance-based predictions of residue contacts and inter-residue distances are an increasingly popular data type in protein bioinformatics. Here we present ConPlot, a web-based application for convenient display and analysis of contact maps and distograms. Integration of predicted contact data with other predictions is often required to facilitate inference of structural features. ConPlot can therefore use the empty space near the contact map diagonal to display multiple coloured tracks representing other sequence-based predictions. Popular file formats are natively read and bespoke data can also be flexibly displayed. This novel visualization will enable easier interpretation of predicted contact maps.Availability and implementationavailable online at www.conplot.org, along with documentation and examples. Alternatively, ConPlot can be installed and used locally using the docker image from the project’s Docker Hub repository. ConPlot is licensed under the BSD 3-Clause.Supplementary informationSupplementary data are available at Bioinformatics online.