Pixel: an open source solution for annotation, storage, mining and integration of multi-omics data in biology

Welcome to the penultimate chapter in this journey about continuous integration and continuous deployment. So far in this book, we have gone over a copious number of topics related to building and automated testing and deploying of a microservice using many popular tools in the Java and open source ecosystem. Honestly, there is not much left to talk about as it relates to the basics of continuous integration and deployment of microservices. There is however one more topic that we would be remiss to not mention, and that is building and deploying our microservice as a Docker container onto a Kubernetes cluster. It’s not terribly complicated to do this, but we’ll break this last topic up into two separate blog chapters. In this chapter we’ll focus on building our Docker container.

During the past two decades, computational analysis has become paramount for biological research. Advancements in high-throughput methods and computational tools resulted in the generation of large amounts of data from different omics fields (multi-omics), such as genomics, epigenomics, transcriptomics, and metabolomics. This plethora of large scale and diverse omics data is being driven by the understanding that a single-omic type does not provide adequate information and integrative analysis of multi-omics data is optimal to gain sufficiently meaningful insights into the actual biological mechanisms. Although various open-source tools have been developed for this purpose, multi-omics data integration and analysis are still beset by a number of problems, including software compatibility, complex parameter selection and creation of functional pipelines with multiple steps of analyses. In this work we present GenOptics, a novel visual analytics platform that aims to facilitate the integration and subsequent analysis of diverse multi-omics datasets as well as meta-data (e.g., clinical data), through a fully interactive environment. The platform comprises of two separate parts. The first incorporates asynchronous analyses of Next-Generation Sequencing raw data, including RNA-, Whole exome-, and ChIP-Seq, using workflows implemented with the Common Workflow Language (doi:10.6084/m9.figshare.3115156.v2) and Docker containers to automate software installation and confer cross-platform portability. The second part constitutes the analytical platform itself, designed to facilitate the execution of robust bioinformatics analyses by life scientists with minimal or no knowledge of programming. GenOptics constitutes an open source (https://genoptics.github.io/), computational biology platform for novel pattern and biomarker detection.

Peer code review is an effective method to reduce the number of defects and maintain source code integrity. Peer re-views in most of the Open Source Software (OSS) communities are conducted via mailing list, which are difficult to manage at times. Code review tools aim to ease the review process and keep track of the review requests. In this paper, we describe preliminary results of our study to evaluate code review process using a popular open source code review tool (ReviewBoard) in OSS communities. Some of our study findings are similar to the findings of previous studies on code reviews. In the projects under our study, we found that, most of the revisions are not submitted for peer review. More than 80% of the review requests are responded by two or less number of reviewers. Top committers of the projects are also top contributors of code reviews. Most of the review requests get prompt feedback within a day; however, some requests might wait for feed-back for a long time. Most importantly, we have identified some interesting directions for future research.

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

Plagiarism is one form of academic dishonesty, which is often done by students in programming classes. In a large class, detecting plagiarism manually is both difficult and time-consuming, especially due to the numerous modifications of the source code to conceal the cheating.We designed and developed Deimos, a prototype of a source code plagiarism detector, which can be extended to handle other programming languages, simply by implementing new scanners and parsers. Deimos works in two steps: (1) parsing source code and transforming it into tokens, and then (2) comparing each pair of token strings obtained in the first step using Running Karp-Rabin Greedy String Tiling algorithm. Instructor can access Deimos via a web application interface that receives input parameters, triggers a background process, and displays the result. The web interface offers user friendliness while the background process prevents timeout and reduces bandwidth consumption. This approach was chosen since Deimos is intended to be used for processing more than 100 source code. The web application was implemented using PHP, while Java was used to implement the backend application, which is responsible for the background process.Unit test, functional test, and nonfunctional test has been conducted. Detection time is 1 hour for processing 100 samples of beginner's source code taken from real assignment of our programming class where the average length of source code is 150 lines. This code similarity detector could also be used for other pedagogical tools, such as autograder, which checks consistency of source code based on a template or solution.

Code review is an important part of the software development process. Recently, many open source projects have begun practicing code review through “modern” tools such as GitHub pull-requests and Gerrit. Many commercial software companies use similar tools for code review internally. These tools enable the owner of a source code change to request individuals to participate in the review, i.e., reviewers. However, this task comes with a challenge. Prior work has shown that the benefits of code review are dependent upon the expertise of the reviewers involved. Thus, a common problem faced by authors of source code changes is that of identifying the best reviewers for their source code change. To address this problem, we present an approach, namely cHRev , to automatically recommend reviewers who are best suited to participate in a given review, based on their historical contributions as demonstrated in their prior reviews. We evaluate the effectiveness of cHRev on three open source systems as well as a commercial codebase at Microsoft and compare it to the state of the art in reviewer recommendation. We show that by leveraging the specific information in previously completed reviews (i.e.,quantification of review comments and their recency), we are able to improve dramatically on the performance of prior approaches, which (limitedly) operate on generic review information (i.e., reviewers of similar source code file and path names) or source coderepository data. We also present the insights into why our approach cHRev outperforms the existing approaches.

Background: Modern Code Reviews (MCR) are frequently adopted when assuring code and design quality in continuous integration and deployment projects. Although tiresome, they serve a secondary purpose of learning about the software product. Aim: Our objective is to design and evaluate a support tool to help software developers focus on the most important code fragments to review and provide them with suggestions on what should be reviewed in this code. Method: We used design science research to develop and evaluate a tool for automating code reviews by providing recommendations for code reviewers. The tool is based on Transformer-based machine learning models for natural language processing, applied to both programming language code (patch content) and the review comments. We evaluate both the ability of the language model to match similar lines and the ability to correctly indicate the nature of the potential problems encoded in a set of categories. We evaluated the tool on two open-source projects and one industry project. Results: The proposed tool was able to correctly annotate (only true positives) 35%–41% and partially correctly annotate 76%–84% of code fragments to be reviewed with labels corresponding to different aspects of code the reviewer should focus on. Conclusion: By comparing our study to similar solutions, we conclude that indicating lines to be reviewed and suggesting the nature of the potential problems in the code allows us to achieve higher accuracy than suggesting entire changes in the code considered in other studies. Also, we have found that the differences depend more on the consistency of commenting rather than on the ability of the model to find similar lines.

Abstract—In this article I have deployed a web-based applica- tion to Docker using Amazon web service (EC2 Instance). Elastic Cloud Compute service is used to the run the docker applications on scalable clusters. Amazon EC2 is fully managed and secured Container orchestration service that makes to deploy, manage and scale Dockerized web application. Docker containers on an EC2 instance to run a university management system website on the Amazon Web Services (AWS) cloud. For educational institutions, a university management system is a crucial tool that enables administrators to manage student records, course information, and other crucial data. The introduction to Docker, a containerization technology that makes it simple to build and manage web applications, follows a discussion of the advantages of hosting a university management system website on the cloud. The next section of the paper focuses on the AWS EC2 service and its capabilities, such as its capability for flexible computing and scalability. The paper also offers a step-by-step tutorial on how to use Docker to deploy a university administration system website on an EC2 instance, including instructions on how to set up the EC2 instance, install and configure Docker, and deploy the website code in a Docker container. Elastic Load Balancer and Auto Scaling, two AWS services, are used in the process of setting up security and scalability for the website. The benefits of using Docker to host a university administration system website on AWS EC2 are outlined in the paper’s conclusion, along with predictions for the development of cloud-based web application hosting. This research paper offers a thorough overview of the steps involved in hosting a university management system website using Docker on AWS EC2, making it a useful tool for educational institutions looking to improve their information management procedures and take advantage of cloud computing and Docker- Containerization.

<p>A typical hurdle faced by scientists when it comes to process data is the installation and maintenance of software tools: the installation procedures are sometimes poorly documented, while there is often several dependencies that may create incompatibilities issues. In order to make easier the life of scientists and experts, a <em>Virtual Research Environment</em> (VRE) is being developed in the frame of SeaDataCloud project.</p><p>The goal is to provide them with a computing environment where the tools are already deployed and datasets are available for direct processing. In the context of SeaDataCloud, the tools are:</p><ul><li>WebODV, able to perform data reading, quality check, subsetting, among many other possibilities.</li> <li>DIVAnd, for the spatial interpolation of in situ measurements.</li> <li>A visualisation toolbox for both the input data and the output, gridded fields.</li> </ul><p><strong>DIVAnd </strong></p><p>DIVAnd (Data-Interpolating Variational Analysis in n dimensions) is a software tool designed to  generate a set of gridded fields from in situ observations. The code is written in Julia a high-performance programming language (https://julialang.org/), particularly suitable for the processing of large matrices. </p><p>The code, developed and improved on a regular basis, is distributed via the hosting platform GitHub: https://github.com/gher-ulg/DIVAnd.jl. It supports Julia-1.0 since its version 2.1.0 (September 2018). </p><p><strong>Notebooks</strong></p><p>Along with the source code, a set of jupyter-notebooks describing the different steps for the production of a climatology are provided, with an increasing level of complexity: https://github.com/gher-ulg/Diva-Workshops/tree/master/notebooks.</p><p><strong>Deployment in the VRE</strong></p><p>JupyterHub (https://jupyter.org/hub), is a multiple-user instance of jupyter notebooks. It has proven an adequate solution to allow several users to work simultaneously with the DIVAnd tool and it offers different ways to isolate the users. The approach selected in the frame of this project is the Docker containers, in which the software tools, as well as their dependencies, are stored. This solution allows multiple copies of a container to be run efficiently in a system and also makes it easier to perform the deployment in the VRE. The authentication step is also managed by JupyterHub.</p><p><strong>Docker container</strong></p><p>The Docker container is distributed via Docker Hub (https://hub.docker.com/r/abarth/divand-jupyterhub) and includes the installation of:</p><ul><li>The Julia language (currently version 1.3.1);</li> <li>Libraries and tools such as netCDF, unzip, git;</li> <li>Various Julia packages such as PyPlot (plotting library), NCDatasets (manipulation of netCDF files) and DIVAnd.jl.</li> <li>The most recent version of the DIVAnd notebooks.</li> </ul><p>All in all, Docker allows one to provide a standardized computing environment to all users and helped significantly the development of the VRE.</p>

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. Supplementary data are available at Bioinformatics online.

Affect Recognition in Code Review: An In-situ Biometric Study of Reviewer’s Affect

When a web service or application evolves, its database schema --- tables, constraints, and indices --- often need to evolve along with it. Depending on the database, some of these changes require a full table lock, preventing the service from accessing the tables under change. To deal with this, web services are typically taken offline momentarily to modify the database schema. However with the introduction of concepts like Continuous Deployment, web services are deployed into their production environments every time the source code is modified. Having to take the service offline --- potentially several times a day --- to perform schema changes is undesirable. In this paper we introduce QuantumDB--- a tool-supported approach that abstracts this evolution process away from the web service without locking tables. This allows us to redeploy a web service without needing to take it offline even when a database schema change is necessary. In addition QuantumDB puts no restrictions on the method of deployment, supports schema changes to multiple tables using changesets, and does not subvert foreign key constraints during the evolution process. We evaluate QuantumDB by applying 19 synthetic and 95 industrial evolution scenarios to our open source implementation of QuantumDB. These experiments demonstrate that QuantumDB realizes zerodowntime migrations at the cost of acceptable overhead, and is applicable in industrial continuous deployment contexts.

Predicting defect in software is a complicated process and time-consuming. AI-Based software defect predictor can predict 75% defect in software and help developer team to detect and to fix defect module before performing unit testing/system testing by quality assurance. Some research tried to construct prediction model using other project datasets, which is called cross-project defect prediction. Nevertheless, the project should have the same software metric attribute. Recently, web application takes a crucial part in human life, for that reason assuring the quality of web application is very serious. This research will implement defect prediction on the petstore web application with other project datasets. CK OO metric is employed as the parameter. Naive bayes algorithm is an effortless and successful algorithm for predicting defect on software. The objective of this research is to apply naive bayes algorithm in cross-project defect prediction for the web application using pandas and scikit-learn. The outcome of this research is naive bayes algorithm has an good accuracy level of 72.30% – 89.30% and slightly low false alarm around by 5% – 26.67%. However, it has low precision and recall score, around 12,5% – 25% and 20%–60%. Then, naive bayes algorithm predicting more defect module on software than code review.

The Center for Information Biology and DNA Data Bank of Japan (CIB-DDBJ) has operated biological databases since 1987 in collaboration with NCBI and EBI. As one of the three major public databases, CIB-DDBJ has run four primary databases DDBJ, CIBEX, DDBJ Trace Archive (DTA), and DDBJ Read Archive (DRA) to collect, archive, and provide various kinds of biological data. As the massively parallel new sequencing platforms are increasingly in use, huge amounts of the raw data have been produced. To archive these raw data, we at CIB-DDBJ began operating a new repository, the DDBJ Read Archive (DRA). To accommodate efficiently the processed data as well, we have developed a new pipeline, the DDBJ Read Annotation Pipeline that deals with both data submission and analysis. For data produced by the next generation platforms, the three archives DRA, DDBJ, and CIBEX, which are interconnected by the pipeline, collect the raw, processed sequence, and quantitative data, respectively. The public biological databases at CIB-DDBJ, EBI, and NCBI will together construct world-wide archives for biological data by data sharing to accelerate research in life sciences in the era of next generation sequencing technologies.

The article presents a methodological approach to assessing the security of software components within the Moodle-based distance learning system using automated static source code analysis methods. The increasing importance of securing educational information systems is emphasized in light of the widespread adoption of LMS platforms that process personal and service-related data of participants in the educational process, including assessment results. Moodle is one of the most popular open source solutions used in university environments, and its modular architecture requires regular security audits due to continuous functional expansion. Within the scope of the study, a critically important module — moodle-tool_componentlibrary – was selected for analysis. This module is responsible for the visual unification of interface elements within the LMS and affects the operation of related components. Using SonarQube (v9.9 LTS) and SonarScanner, deployed in an isolated WSL2 environment via Docker containers, a comprehensive static analysis was performed on 5,892 lines of source code. The analysis revealed 589 issues, distributed as follows: 72 reliability-related problems, 532 maintainability issues, and 2 critical points marked as security hotspots requiring manual review. Complete absence of unit testing (0 % coverage) and an excessive level of code duplication (33.4 %) were also identified, posing risks during functional modification. Based on the obtained metrics, a methodology for calculating a generalized security coefficient was developed. This result indicates the need for refactoring prior to deployment in a production environment and highlights the effectiveness of integrating open source static analysis tools into CI/CD pipelines and the overall maintenance process of LMS platforms.