ABSTRACT Objectives Cookiecutter is a popular, mature open‐source Python library for automating the creation of customized projects from templates. This kind of scaffolding is useful for enabling reuse, encapsulating expertise, achieve uniformity, and facilitating project creation for a range of languages and domains, including microservices, web applications, and data science. Despite their success, there is a lack of descriptions of general‐purpose project templating tools such as Cookiecutter in the literature. The objective of the paper is to provide a description of Cookiecutter that is useful for researchers and practitioners. Methods Our work is informed by our own use of Cookiecutter in the context of an industrial project. We describe Cookiecutter with the help of four different research questions. The first two relate to how Cookiecutter works (RQ1) and how it is used (RQ2). The latter two are concerned with providing guidance to users of Cookiecutter (RQ3) and identifying challenges that they may face (RQ4). Result Our answer to question RQ1 provides a succinct, high‐level description of the structure of Cookiecutter templates and Cookiecutter's execution semantics. For question RQ2, we provide an analysis of the 100 most popular Cookiecutter templates on GitHub and descriptions of three applications of Cookiecutter in different domains. For question RQ3, we identify quality attributes for Cookiecutter projects together with best practice recommendations. For question RQ4, our discussion of challenges is structured around different lifecycle activities related to the overall management of Cookiecutter templates. The potential for research results in related areas such as software product lines, feature and variability modeling, and model‐driven engineering to help address these challenges is highlighted. Conclusion The paper provides a comprehensive discussion of Cookiecutter, a successful general‐purpose project templating tool with demonstrated industrial use. The discussion covers fundamental and practical aspects of Cookiecutter and thus targets practitioners as well as researchers interested in general‐purpose templating and Cookiecutter in particular.

BACKGROUND: The human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data. RESULTS: We present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results. CONCLUSION: IPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl , while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit .

BACKGROUND: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. RESULTS: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. CONCLUSION: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.