MotivationReliable tools and software for penetrance (age-specific risk among those who carry a genetic variant) estimation are critical to improving clinical decision making and risk assessment for hereditary syndromes. However, there is a lack of easily usable software for penetrance estimation in family-based studies that implements a Bayesian estimation approach.ResultsWe introduce penetrance, an open-source R package available on CRAN, to estimate age-specific penetrance using family-history pedigree data. The package uses a Bayesian estimation approach, allowing for the incorporation of prior knowledge through the specification of priors for the parameters of the carrier distribution. It also includes options to impute missing ages during the estimation process, addressing incomplete age information which is not uncommon in pedigree datasets. Our open-source software provides a flexible and user-friendly tool for researchers to estimate penetrance in complex family-based studies, facilitating improved genetic risk assessment in hereditary syndromes.Availability and implementationThe penetrance package is freely available on CRAN. Source code and documentation are available at https://github.com/nicokubi/penetrance.

SummaryWelly is a web-based tool designed to simplify the visualization and analysis of growth curves from 96- and 384-well plates, addressing the limitations of existing commercial and coding-based solutions. Users can upload plate reader data in CSV or Excel format, easily select sample names and replicates and Welly generates interactive growth curves displaying the mean and standard deviation of triplicates. Additional features include heat map visualizations of maximum values, and downloadable interactive graphs of publication-quality figures and statistics files containing area under curve and max growth rate value of replicates.Availability and implementationWelly is freely available at https://synbioexplorer.pythonanywhere.com, providing an easy-to-use interface accessible to all. All the code is publicly available at the github repository https://github.com/SynBioExplorer/Welly under the MIT license. The website will remain freely accessible for at least 2 years post publication, likely longer.

MotivationSingle sequencing technology (scRNA-seq) enables the study of gene regulation at a single cell level. Although many sc-RNA-seq protocols have been established, they have varied in technical complexity, sequencing depth and multimodal capabilities leading to shared limitations in data interpretation due to a lack of standardized preprocessing and consistent data reproducibility. While plate based techniques such as Massively Parallel RNA Single cell Sequencing (MARS-seq2.0) provide reference data on the cells that will be sequenced, the data format limits the possible analysis. Here, we focus on the standardization of MARS-seq analysis and its applicability to RNA velocity.ResultsWe have taken the original MARS-seq2.0 pipeline and revised it to enable implementation using the nf-core framework. By doing so, we have simplified pipeline execution, enabling a streamlined application with increased transparency and scalability. We have incorporated additional checkpoints to verify experimental metadata and improved the pipeline by implementing a custom workflow for RNA velocity estimation. The pipeline is part of the nf-core bioinformatics community and is freely available at https://github.com/nfcore/marsseq with data analysis at https://github.com/brickmanlab/proks-et-al-2023.Availability and implementationWe introduce an updated preprocessing pipeline for MARS-seq experiments following state-of-the-art guidelines for scientific software development with the added ability to infer RNA velocity.

MotivationHierarchical data structures are prevalent across several research fields, as they represent an organized and efficient approach to study complex interconnected systems. Their significance is particularly evident in microbiome analysis, where microbial communities are classified at various taxonomic levels using phylogenetic trees. In light of this trend, the R/Bioconductor community has established a reproducible analytical framework for hierarchical data, which relies on the generic and optimized TreeSummarizedExperiment data container. However, this framework requires basic programming skills.ResultsTo reduce the entry requirements, we developed iSEEtree, an R package, which provides a visual interface for the analysis and exploration of TreeSummarizedExperiment objects, thereby expanding the interactive graphics capabilities of related work to hierarchical structures. This way, users can interactively explore several aspects of their data without the need for an extensive knowledge of R programming. We describe how iSEEtree enables the exploration of hierarchical multi-table data and demonstrate its functionality with applications to microbiome analysis.Availability and implementationiSEEtree was implemented in the R programming language and is available on Bioconductor at https://bioconductor.org/packages/iSEEtree under an Artistic 2.0 license.

MotivationCoiled coils are a widespread structural motif consisting of multiple α-helices that wind around a central axis to bury their hydrophobic core. While AlphaFold has emerged as an effective coiled-coil modeling tool, capable of accurately predicting changes in periodicity and core geometry along coiled-coil stalks, it is not without limitations, such as the generation of spuriously bent models and the inability to effectively model globally non-canonical-coiled coils. To overcome these limitations, we investigated whether dividing full-length sequences into fragments would result in better models.ResultsWe developed CCfrag to leverage AlphaFold for the piece-wise modeling of coiled coils. The user can create a specification, defined by window size, length of overlap, and oligomerization state, and the program produces the files necessary to run AlphaFold predictions. The structural models and their scores are then integrated into a rich per-residue representation defined by sequence- or structure-based features. Our results suggest that removing coiled-coil sequences from their native context can improve prediction confidence and results in better models. In this article, we present various use cases of CCfrag and propose that fragment-based prediction is useful for understanding the properties of long, fibrous coiled coils by revealing local features not seen in full-length models.Availability and implementationThe program is implemented as a Python module. The code and its documentation are available at https://github.com/Mikel-MG/CCfrag.

MotivationNMR-based metabolomics is a field driven by technological advancements, necessitating the use of advanced preprocessing tools. Despite this need, there is a remarkable scarcity of comprehensive and user-friendly preprocessing tools in Python. To bridge this gap, we have developed Protomix—a Python package designed for metabolomics research. Protomix offers a set of automated, efficient, and user-friendly signal-preprocessing steps, tailored to streamline and enhance the preprocessing phase in metabolomics studies.ResultsThis package presents a comprehensive preprocessing pipeline compatible with various data analysis tools. It encompasses a suite of functionalities for data extraction, preprocessing, and interactive visualization. Additionally, it includes a tutorial in the form of a Python Jupyter notebook, specifically designed for the analysis of 1D 1H-NMR metabolomics data related to prostate cancer and benign prostatic hyperplasia.Availability and implementationProtomix can be accessed at https://github.com/mzniber/protomix and https://protomix.readthedocs.io/en/latest/index.html.

Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task. Here, we introduce splicekit, a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis. The software splicekit is open-source and available at Github (https://github.com/bedapub/splicekit) and via the Python Package Index.

Genomic signal processing (GSP), which transforms biomolecular sequences into discrete signals for spectral analysis, has provided valuable insights into DNA sequence, structure, and evolution. However, challenges persist with spectral representations of variable-length sequences for tasks like species classification and in interpreting these spectra to identify discriminative DNA regions. We introduce SpecGMM, a novel framework that integrates sliding window-based Spectral analysis with a Gaussian Mixture Model to transform variable-length DNA sequences into fixed-dimensional spectral representations for taxonomic classification. SpecGMM's hyperparameters were selected using a dataset of plant sequences, and applied unchanged across diverse datasets, including mitochondrial DNA, viral and bacterial genome, and 16S rRNA sequences. Across these datasets, SpecGMM outperformed a baseline method, with 9.45% average and 35.55% maximum improvement in test accuracies for a Linear Discriminant classifier. Regarding interpretability, SpecGMM revealed discriminative hypervariable regions in 16S rRNA sequences-particularly V3/V4 for discriminating higher taxa and V2/V3 for lower taxa-corroborating their known classification relevance. SpecGMM's spectrogram video analysis helped visualize species-specific DNA signatures. SpecGMM thus provides a robust and interpretable method for spectral DNA analysis, opening new avenues in GSP research. SpecGMM's source code is available at https://github.com/BIRDSgroup/SpecGMM.

We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments. Extensive documentation, installation steps, tutorials, and examples are available at https://tp-watson-python-cfba.readthedocs.io/en/. The py_cFBA python package is available at https://pypi.org/project/py-cfba/.

We recently developed EagleImp, a free software that combines genotype phasing and imputation in a single tool. By introducing algorithmic and technical improvements we accelerated the classical two-step approach using Eagle2 and PBWT. Here, we demonstrate how to use field-programmable gate arrays (FPGAs) to accelerate EagleImp even further by a factor of up to 93% without loss of phasing and imputation quality. Due to the speed advantage over a not accelerated processor-based implementation, the FPGA extension of EagleImp allows the user to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality. EagleImp and its FPGA extension are freely available at https://github.com/ikmb/eagleimp and https://github.com/ikmb/eagleimp-fpga.