Source code similarity detection tools are crucial for preventing and identifying plagiarism in programming courses. While tools like JPlag and Moss are effective, their complexity often hinders widespread adoption. To address this, we developed Dolos, a user-friendly source code similarity detection pipeline that enhances the user experience with interactive dashboards. The Dolos ecosystem includes software libraries, a CLI tool, a web UI, a web server, and API server enabling seamless integration with educational environments. The Dolos web server allows instructors to perform plagiarism detection directly within their browsers, eliminating the need for additional software installation. A publicly available instance and the option for private instances further enhance accessibility. Dolos' API facilitates integration as a microservice within programming exercise platforms like Dodona, A+ and Radar, and Codio, showcasing its versatility and effectiveness. At Ghent University, Dolos is integral to the plagiarism detection strategy, with visualisations serving as both a detection tool and a deterrent. The focus on user experience, flexibility, and comprehensive documentation has attracted scholars to use Dolos for innovative applications, even outside of the educational field. We invite instructors to explore Dolos and integrate it within their educational platforms for programming assignments.

Educators often spend a significant amount of time answering student coding questions, which can lead to rushed or incomplete responses. Additionally, generative AI tools are starting to and will play an increasing role in students' careers. These tools are easy to use and can provide heaps of information almost instantly. However, they often generate answers that provide full assignment solutions rather than guiding students towards the correct solution, which can be detrimental to their learning process. To address this issue, we are exploring the potential of large language models (LLMs) in improving student support by generating draft responses. These drafts are designed to provide students with meaningful guidance without giving away direct solutions. To evaluate these drafts, an LLM-as-a-judge is employed to compare the generated answers from various LLMs, different prompts, and best available human answers against a ground truth dataset. We present the evaluation process using an LLM-as-a-judge based benchmark, discuss the results obtained by different models and prompts, and compare them to the best available human responses. These evaluations give an indication of how LLMs can aid in computer science education, by reducing the time needed to answer questions and increasing both the accuracy and effectiveness of responses.

BACKGROUND: Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets. A sparse suffix array (SSA) retains only suffixes at every k-th position in the text, where k is the sparseness factor. While sparse suffix arrays offer significant memory savings compared to full suffix arrays, they typically still require the construction of a full suffix array prior to a sampling step, resulting in substantial memory overhead during the construction phase. RESULTS: We present an alternative method to directly construct the sparse suffix array using a simple, yet powerful text encoding. This encoding reduces the input text length by grouping characters, thereby enabling direct SSA construction by extending the widely used Libsais library. This approach bypasses the need to construct a full suffix array, reducing memory usage and construction time by 50 to 75% when building a sparse suffix array with sparseness factor 3 or 4 for various nucleotide and amino acid datasets. Depending on the alphabet size, similar gains can be achieved for sparseness factors up to 8. For higher sparseness factors, comparable performance improvements can be obtained by constructing the SSA using a suitable divisor of the desired sparseness factor, followed by a subsampling step. The method is particularly effective for applications with small alphabets, such as a nucleotide or amino acid alphabet. An open-source implementation of this method is available on GitHub, enabling easy adoption for large-scale bioinformatics applications. CONCLUSIONS: We introduce an efficient method for the construction of sparse suffix arrays for large datasets. Central to this approach is the introduction of a simple text transformation, which then serves as input to Libsais. This method reduces the length of both the input text and the resulting suffix array by a factor of k, which improves execution time and memory usage significantly.

With the increasing demand for programming skills comes a trend towards more online programming courses and assessments. While this allows educators to teach larger groups of students, it also opens the door to dishonest student behaviour, such as copying code from other students. When teachers use assignments where all students write code for the same problem, source code similarity tools can help to combat plagiarism. Unfortunately, teachers often do not use these tools to prevent such behaviour.

Dodona (dodona.ugent.be) is an intelligent tutoring system for learning computer programming, statistics and data science. It bridges the gap between assessment and learning by providing real-time data and feedback to help students learn better, teachers teach better and educational technology become more effective.

SUMMARY: The Unipept Visualizations library is a JavaScript package to generate interactive visualizations of both hierarchical and non-hierarchical quantitative data. It provides four different visualizations: a sunburst, a treemap, a treeview and a heatmap. Every visualization is fully configurable, supports TypeScript and uses the excellent D3.js library. AVAILABILITY AND IMPLEMENTATION: The Unipept Visualizations library is available for download on NPM: https://npmjs.com/unipept-visualizations. All source code is freely available from GitHub under the MIT license: https://github.com/unipept/unipept-visualizations.

BACKGROUND: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. RESULTS: This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. CONCLUSIONS: Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation ( https://github.com/unipept/FragGeneScanRs ).

SUMMARY: Unipept is an ecosystem of tools developed for fast metaproteomics data-analysis consisting of a web application, a set of web services (application programming interface, API) and a command-line interface (CLI). After the successful introduction of version 4 of the Unipept web application, we here introduce version 2.0 of the API and CLI. Next to the existing taxonomic analysis, version 2.0 of the API and CLI provides access to Unipept's powerful functional analysis for metaproteomics samples. The functional analysis pipeline supports retrieval of Enzyme Commission numbers, Gene Ontology terms and InterPro entries for the individual peptides in a metaproteomics sample. This paves the way for other applications and developers to integrate these new information sources into their data processing pipelines, which greatly increases insight into the functions performed by the organisms in a specific environment. Both the API and CLI have also been expanded with the ability to render interactive visualizations from a list of taxon ids. These visualizations are automatically made available on a dedicated website and can easily be shared by users. AVAILABILITY AND IMPLEMENTATION: The API is available at http://api.unipept.ugent.be. Information regarding the CLI can be found at https://unipept.ugent.be/clidocs. Both interfaces are freely available and open-source under the MIT license. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines. AVAILABILITY AND IMPLEMENTATION: The web services are freely available at http://api.unipept.ugent.be and are open sourced under the MIT license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.