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Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a high-level. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network's improvements compared to the original autonomous driving system. The code implementing the presented network is available at https://github.com/tonegas/safe-speed-neural-network with MIT license and at https://zenodo.org/communities/dreams4cars.

Glycans play an important role in many biochemical processes, including protein function and cell signaling. Mass spectrometry (MS) provides the potential for high-throughput, high-sensitivity analysis of glycans but relies heavily on computational interpretation of experimental results. Open-source, stand-alone algorithms for de novo glycan MS analysis are few. One such algorithm, Sweet-SEQer, is available in Python. Glycan analysis of mass spectra can easily involve high volumes of data where Python's performance in time and memory is a noticeable bottleneck. This manuscript describes C-SEQer, a new implementation of the Sweet-SEQer algorithm in C++, which produces the same output as the original algorithm in approximately 15-fold less time with substantially less memory usage. The implementation is freely available with an MIT license.

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://github.com/achon/sramm., Comment: 7 pages, 2 figures

Fast-scan cyclic voltammetry at carbon fiber microelectrodes measures low concentrations of analytes in biological systems. There are ongoing efforts to simplify FSCV analysis and several custom platforms are available for filtering and multi-modal analysis of FSCV signals but there is no single, easily accessible platform that has capacity for all these features. Here we present The Analysis Kid: a free, open-source cloud application that does not require a specialized runtime environment and is easily accessible via common browsers. We show that a user-friendly interface can analyze multi-platform file formats to provide multimodal visualization of FSCV color plots with digital background subtraction. We highlight key features that allow interactive calibration and parametric analysis via peak finding algorithms to automatically detect the maximum amplitude, area under the curve and clearance rate of the signal. Finally, The Analysis Kid enables semi-automatic fitting of data with Michaelis Menten kinetics with single or dual reuptake models. The Analysis Kid can be freely accessed at https://analysis-kid.herokuapp.com/. The web application code is found, under an MIT license, at https://github.com/sermeor/The-Analysis-Kid.

Understanding multicomponent binding interactions in protein-ligand, protein-protein and competition systems is essential for fundamental biology and drug discovery. Hand deriving equations quickly becomes unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formation culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at: https://github.com/stevenshave/pybindingcurve as Python source code accompanied by examples and as an easily installable package within the Python Package Index.

The package implements nonparametric (smooth) regression for spherical data in , and is freely available from the Comprehensive Archive Network (CRAN), licensed under the MIT License. It can be use...

Motivation Pangenomics evolved since its first applications on bacteria, extending from the study of genes for a given population to the study of all of its sequences available. While multiple methods are being developed to construct pangenomes in eukaryotic species there is still a gap for efficient and user-friendly visualization tools. Emerging graph representations come with their own challenges, and linearity remains a suitable option for user-friendliness. Results We introduce Panache, a tool for the visualization and exploration of linear representations of gene-based and sequence-based pangenomes. It uses a layout similar to genome browsers to display presence absence variations and additional tracks along a linear axis with a pangenomics perspective. Availability and implementation Panache is available at github.com/SouthGreenPlatform/panache under the MIT License.

Bioinformatic analysis—such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, paired-end aware quality trimming and filtering of sequencing reads, file format conversion, and processing and analysis—is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses; however, the lack of a unified toolkit that conducts all these analyses can be a barrier in workflows. To address this obstacle, we introduce BioKIT, a versatile toolkit for the UNIX shell environment with 40 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we assessed the quality and characteristics of 901 eukaryotic genome assemblies, calculated alignment summary statistics for 10 phylogenomic data matrices, determined relative synonymous codon usage across 171 fungal genomes including those that use alternative genetic codes, and demonstrate that a novel metric, gene-wise relative synonymous codon usage, can accurately estimate gene-wise codon optimization. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwykbiokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

SummaryProperly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows to easily organize, retrieve, and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. Additionally, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie’s remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS repository from GalaxyProject.org, with mirrors across the United States, Canada, Europe, and Australia, enabling easy use outside of Galaxy.Availability and implementationThe ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 20.05, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license.

Summary Expression quantitative trait loci (eQTLs) characterize the associations between genetic variation and gene expression to provide insights into tissue-specific gene regulation. Interactive visualization of tissue-specific eQTLs or splice QTLs (sQTLs) can facilitate our understanding of functional variants relevant to disease-related traits. However, combining the multi-dimensional nature of eQTLs/sQTLs into a concise and informative visualization is challenging. Existing QTL visualization tools provide useful ways to summarize the unprecedented scale of transcriptomic data but are not necessarily tailored to answer questions about the functional interpretations of trait-associated variants or other variants of interest. We developed FIVEx, an interactive eQTL/sQTL browser with an intuitive interface tailored to the functional interpretation of associated variants. It features the ability to navigate seamlessly between different data views while providing relevant tissue- and locus-specific information to offer users a better understanding of population-scale multi-tissue transcriptomic profiles. Our implementation of the FIVEx browser on the EBI eQTL catalogue, encompassing 16 publicly available RNA-seq studies, provides important insights for understanding potential tissue-specific regulatory mechanisms underlying trait-associated signals. Availability and implementation A FIVEx instance visualizing EBI eQTL catalogue data can be found at https://fivex.sph.umich.edu. Its source code is open source under an MIT license at https://github.com/statgen/fivex. Supplementary information Supplementary data are available at Bioinformatics online.

Summary Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace. Availability and implementation ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy. Supplementary information Supplementary data are available at Bioinformatics online.

Objectives To enhance reproducible research by creating a broadly accessible, free, open-source software tool for connecting Microsoft Word to statistical programs (R/R Markdown, Python, SAS, Stata) so that results may be automatically updated in a manuscript. Materials and Methods We developed StatTag for Windows as a Microsoft Word plug-in using C# and for macOS as a native application using Objective-C. Source code is available under the MIT license at https://github.com/stattag. Results StatTag links analysis file(s) (R/R Markdown, SAS, Stata, or Python) and a Word document, invokes the statistical program(s) to obtain results, and embeds selected output in the document. StatTag can accommodate multiple statistical programs with a single document and features an interface to view, edit, and rerun statistical code directly from Word. Discussion and Conclusion StatTag may facilitate reproducibility within increasingly multidisciplinary research teams, improve research transparency through review and publication, and complement data-sharing initiatives.

Discusses the origin and history of the “MIT License.” This license has become a popular way of releasing copyrighted computer programs for others to use without requiring signatures or a license fee. The quoted words refer to a group of software licenses that have common ancestry and guiding principles but varying wordings. There has been some recent discussion about the origin of the “MIT License,” but that discussion has been inconclusive because authoritative historical documentation has been missing or hard to find. This note provides some of that history and documentation. Being composed 35 years after the events involved, it relies primarily on my often-flaky recollections but it also offers some relevant supporting documentation found in my files and in online archives.

A JavaScript framework named 'jsPsych' developed by de Leeuw (2015) is widely used for conducting Web-based experiments, and its functionality can be enhanced by using plugins. This article introduces a new jsPsych plugin which enables experimenters to set different onset times for geometric figures, images, sounds, and moving objects, and present them synchronized with the refresh of the display. Moreover, this study evaluated the stimulus onset asynchronies (SOAs) using visual and audio stimuli. The results showed that: (i) the deviations from the intended SOAs between two visual stimuli were less than 10 ms, (ii) the variability across browser-computer combinations was reduced compared with the no-plugin condition, and (iii) the variability of the SOAs between visual and audio stimuli was relatively large (about 50 ms). This study concludes that although the use of audio stimuli is somewhat limited, the new plugin provides experimenters with useful and accurate methods for conducting psychophysical experiments online. The latest version of the plugin can be downloaded freely from https://jspsychophysics.hes.kyushu-u.ac.jp under the MIT license.

MotivationWith Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. A lot of different variant callers are publicly available and usually do a good job at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-reads based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy.ResultsWe present UMI-Gen, a UMI-based reads simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user.AvailabilityThe entire pipeline is available athttps://gitlab.com/vincent-sater/umigen-masterunder MIT license.Contactvincent.sater@gmail.com

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

For two decades, GWAS identified individual variants associated with risk for complex diseases. These associations can be combined into polygenic scores (PGS) aiming at quantifying an individual’s risk to disease, inform on prognosis and even treatment response (Lambert et al., 2019). Broadly, PGS use summary statistics produced by GWAS to calculate a weighted sum of trait-associated alleles carried by each individual, in which the weights correspond to the per-allele size effects. Initially used to validate associations with disease and uncover interactions between variants, PGS have been more challenging to implement in the clinic. In 2020, over 1400 publications on PGS appeared in PubMed, raising the need for a standardized distribution of studies’ key data, assuring their wide evaluation and accurate use. The Polygenic Score (PGS) Catalog, created in 2019, is a publicly available, manually curated database of PGS and relevant metadata, that responds to this need (Lambert et al., 2020). Its current release [date 2021-02-03] includes data from 133 publications and 721 PGS associated with 194 traits. Currently, data is accessed via three ways: (i) the web graphical user interface (GUI); (ii) by downloading database dumps; and (iii) the recent PGS Catalog representational state transfer (REST) application programming interface (API), the preferred method for batch analyses. info:eu-repo/semantics/publishedVersion

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 Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments’ results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case. Results We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS’ focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases. Conclusions CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.

This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography-mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub ( https://github.com/arseha/peakonly ) under an MIT license.

Motivation Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several parameters, the choice of which can be challenging and subjective. Results In this work, we propose DeepSinse, an easily trainable and useable deep neural network that can detect single molecules with little human input and across a wide range of signal-to-noise ratios. We validate the neural network on the detection of single bursts in simulated and experimental data and compare its performance with the best-in-class, domain-specific algorithms. Availabilityand implementation Ground truth ROI simulating code, neural network training, validation code, classification code, ROI picker, GUI for simulating, training and validating DeepSinse as well as pre-trained networks are all released under the MIT License on www.github.com/jdanial/DeepSinse. The dSTORM dataset processing code is released under the MIT License on www.github.com/jdanial/StormProcessor. Supplementary information Supplementary data are available at Bioinformatics online.

Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online.

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during nascent transcription. In 2012, Ginno et al. introduced the first R-loop mapping method, DNA:RNA immunoprecipitation (DRIP) sequencing. Since that time, dozens of studies have implemented R-loop mapping and new high-resolution techniques have been developed. The resulting datasets have tremendous potential to reveal the causes and consequences of R-loops genome-wide. However, poor quality and variability between mapping approaches pose serious barriers to the meta-analysis of these data. In our recent work, we reprocessed 693 R-loop mapping samples, devising new quality methods, defining a set of high-confidence mapping samples, and then deriving R-loop regions, consensus sites of R-loop formation. This analysis yielded the largest R-loop data resource to date along with novel computational approaches for R-loop mapping analysis. Now, we introduce RLBase, an innovative web server which builds upon those data and software by providing users with the capability to (1) explore hundreds of public R-loop mapping datasets, (2) explore consensus R-loop regions, (3) analyze user-supplied datasets to generate an HTML quality report, and (4) download all the processed data for the 693 samples we previously reprocessed and standardized. In addition to RLBase, we also describe the other software which, along with RLBase, provides a computational framework for R-loop bioinformatics. RLBase, and the rest of these software (termed “RLSuite”), are provided freely under an MIT license and made publicly available: https://gccri.bishop-lab.uthscsa.edu/rlsuite/. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/rlbase/.

The vast majority of human traits, including many disease phenotypes, are affected by alleles at numerous genomic loci. With a continually increasing set of variants with published clinical disease or biomarker associations, an easy-to-use tool for non-programmers to rapidly screen VCF files for risk alleles is needed. We have developed EZTraits as a tool to quickly evaluate genotype data (e.g., from microarrays) against a set of rules defined by the user. These rules can be defined directly in the scripting language Lua, for genotype calls using variant ID (RS number) or chromosomal position. Alternatively, EZTraits can parse simple and intuitive text including concepts like ‘any’ or ‘all’. Thus, EZTraits is designed to support rapid genetic analysis and hypothesis-testing by researchers, regardless of programming experience or technical background. The software is implemented in C++ and compiles and runs on Linux and MacOS. The source code is available under the MIT license from https://github.com/selfdecode/rd-eztraitsContact: manfred@selfdecode.com

Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends.The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

Summary Single-cell DNA template strand sequencing (Strand-seq) enables chromosome length haplotype phasing, construction of phased assemblies, mapping sister-chromatid exchange events and structural variant discovery. The initial quality control of potentially thousands of single-cell libraries is still done manually by domain experts. ASHLEYS automates this tedious task, delivers near-expert performance and labels even large datasets in seconds. Availability and implementation github.com/friendsofstrandseq/ashleys-qc, MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

Background The Sequence Alignment/Map Format Specification (SAM) is one of the most widely adopted file formats in bioinformatics and many researchers use it daily. Several tools, including most high-throughput sequencing read aligners, use it as their primary output and many more tools have been developed to process it. However, despite its flexibility, SAM encoded files can often be difficult to query and understand even for experienced bioinformaticians. As genomic data are rapidly growing, structured, and efficient queries on data that are encoded in SAM/BAM files are becoming increasingly important. Existing tools are very limited in their query capabilities or are not efficient. Critically, new tools that address these shortcomings, should not be able to support existing large datasets but should also do so without requiring massive data transformations and file infrastructure reorganizations. Results Here we introduce SamQL, an SQL-like query language for the SAM format with intuitive syntax that supports complex and efficient queries on top of SAM/BAM files and that can replace commonly used Bash one-liners employed by many bioinformaticians. SamQL has high expressive power with no upper limit on query size and when parallelized, outperforms other substantially less expressive software. Conclusions SamQL is a complete query language that we envision as a step to a structured database engine for genomics. SamQL is written in Go, and is freely available as standalone program and as an open-source library under an MIT license, https://github.com/maragkakislab/samql/.

Chemical reaction schemes are commonly used for visual encapsulation of chemical information. Figures of reaction schemes contain chemical transformations, the chemical species involved, as well as reaction conditions. From a data-mining point of view, they constitute rich sources, densely packed with knowledge. Yet, the challenge of automatically extracting data from them has remained largely untackled. This work presents ReactionDataExtractor, a software tool that can be used for the automatic extraction of information from multistep reaction schemes. Its capabilities include segmentation of reaction steps, regions containing reaction conditions, chemical diagrams, as well as optical character and structure recognition. A combination of rules and unsupervised machine-learning approaches is used, with bespoke detection algorithms that identify arrows, structures, labels, and conditions detection algorithms. It can be used as a low-maintenance tool for database generation capable of extracting data from large quantities of images supplied by the user. On assessment using a self-generated evaluation set, the tool achieved precision and recall metrics of between 67% and 91% in the six core areas of data extraction. The ReactionDataExtractor tool is released under the MIT license and is available to download from http://www.reactiondataextractor.org.

MicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function causes cascading effects leading to pathophenotypes. A better understanding of the role of miRNAs in diseases is essential for human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogs, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. miR2Trait provides resources to compute the disease enrichment of a user-given set of miRNAs and analyze the miRNA profile of a specified diseasome. Reproducible examples demonstrating use-cases for each of these resource components are illustrated. Furthermore we used these tools to construct pairwise miRNA-miRNA and disease-disease enrichment networks, and identified 23 central miRNAs that could underlie major regulatory functions in the human genome. miR2Trait is available as an open-source command-line interface in Python3 (URL: https://github.com/miR2Trait) with a companion wiki documenting the scripts and data resources developed, under MIT license for commercial and non-commercial use. A minimal web-based implementation has been made available at https://sas.sastra.edu/pymir18. Supplementary information is available at: https://doi.org/10.6084/m9.figshare.8288825.v3.

SummaryA key aspect of microbiome research is analysis of longitudinal dynamics using time series data. A method to visualize both the proportional and absolute change in the abundance of multiple taxa across multiple subjects over time is needed. We developed BiomeHorizon, an open-source R package that visualizes longitudinal compositional microbiome data using horizon plots.Availability and ImplementationBiomeHorizon is available at https://github.com/blekhmanlab/biomehorizon/ and released under the MIT license. A guide with step-by-step instructions for using the package is provided at https://blekhmanlab.github.io/biomehorizon/. The guide also provides code to reproduce all plots in this manuscript.Contactlgrienei@umn.edu, blekhman@umn.edu, iafink@uchicago.eduSupplementary informationNone

SummaryVisualization and analysis of genomic repeats is typically accomplished through the use of dot plots; however, the emergence of telomere-to-telomere assemblies with multi-megabase repeats requires new visualization strategies. Here, we introduce StainedGlass which can generate publication quality figures and interactive visualizations that depict the identity and orientation of multi-megabase repeat structures at a genome-wide scale. The tool can rapidly reveal higher-order structures and improve the inference of evolutionary history for some of the most complex regions of genomes.Availability and implementationStainedGlass is implemented using Snakemake and is available open source under the MIT license athttps://mrvollger.github.io/StainedGlass/.Contactmvollger@uw.edu

Highlights • ViBiBa is an open-source sample banking tool. • ViBiBa was purpose built for liquid biopsy specimen. • ViBiBa allows for decentralized storage while promoting collaboration. • ViBiBa's plugin support requires no change in existing data structures. • ViBiBa empowers translational research projects and cohort formation., Background Liquid Biopsy (LB) in the form of e.g., circulating tumor cells (CTCs) is a promising non-invasive approach to support current therapeutic cancer management. However, the proof of clinical utility of CTCs in informing therapeutic decision-making for e.g., breast cancer in clinical trials and associated translational research projects is facing the issues of low CTC positivity rates and low CTC numbers – even in the metastasized situation. To compensate for this dilemma, clinical CTC trials are designed as large multicenter endeavors with decentralized sample collection, processing and storage of products, making data management highly important to enable high-quality translational CTC research. Aim In the DETECT clinical CTC trials we aimed at developing a custom-made, browser-based virtual database to harmonize and organize both decentralized processing and storage of LB specimens and to enable the collection of clinically meaningful LB sample. Methods ViBiBa processes data from various sources, harmonizes the data and creates an easily searchable multilayered database. Results An open-source virtual bio-banking web-application termed ViBiBa was created, which automatically processes data from multiple non-standardized sources. These data are automatically checked and merged into one centralized databank and are providing the opportunity to extract clinically relevant patient cohorts and CTC sample collections. Summary ViBiBa, which is a highly flexible tool that allows for decentralized sample storage of liquid biopsy specimens, facilitates a solution which promotes collaboration in a user-friendly, federalist and highly structured way. The source code is available under the MIT license from https://vibiba.com or https://github.com/asperciesl/ViBiBa

Summary Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project. Availability and implementation DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationBioinformaticians frequently navigate among a diverse set of coordinate systems: for example, converting between genomic, transcript, and protein coordinates. The abstraction of coordinate systems and feature arithmetic allows genomic workflows to be expressed more elegantly and succinctly. However, no publicly available software library offers fully featured interoperable support for multiple coordinate systems. As such, bioinformatics programmers must either implement custom solutions, or make do with existing utilities, which may lack the full functionality they require.ResultsWe present BioCantor, a Python library that provides integrated library support for arbitrarily related coordinate systems and rich operations on genomic features, with I/O support for a variety of file formats.Availability and implementationBioCantor is implemented as a Python 3 library with a minimal set of external dependencies. The library is freely available under the MIT license at https://github.com/InscriptaLabs/BioCantor and on the Python Package Index at https://pypi.org/project/BioCantor/. BioCantor has extensive documentation and vignettes available on ReadTheDocs at https://biocantor.readthedocs.io/en/latest/.

Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (e.g. brain derived, psychiatric, behavioral and physiological variables) and neuroimaging specific data (e.g. brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging-based ML questions, in particular, those queried from large human datasets. Availability and implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app.

Identification and retrieval of genes of interest from genomic data are an essential step for many bioinformatic applications. We present orthofisher, a command-line tool for automated identification and retrieval of genes with high sequence similarity to a query profile Hidden Markov Model sequence alignment across a set of proteomes. Performance assessment of orthofisher revealed high accuracy and precision during single-copy orthologous gene identification. orthofisher may be useful for assessing gene annotation quality, identifying single-copy orthologous genes for phylogenomic analyses, estimating gene copy number, and other evolutionary analyses that rely on identification and retrieval of homologous genes from genomic data. orthofisher comes complete with comprehensive documentation (https://jlsteenwyk.com/orthofisher/), is freely available under the MIT license, and is available for download from GitHub (https://github.com/JLSteenwyk/orthofisher), PyPi (https://pypi.org/project/orthofisher/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/orthofisher).

MomentClosure.jl is a Julia package providing automated derivation of the time-evolution equations of the moments of molecule numbers for virtually any chemical reaction network using a wide range of moment closure approximations. It extends the capabilities of modelling stochastic biochemical systems in Julia and can be particularly useful when exact analytic solutions of the chemical master equation are unavailable and when Monte Carlo simulations are computationally expensive. MomentClosure.jl is freely accessible under the MIT license. Source code and documentation are available at https://github.com/augustinas1/MomentClosure.jl, Comment: 2 pages, 1 figure

MotivationTechnical progress in computer hardware made it possible to access and process large amounts of data even on budget workstations. Therefore new or existing alignment algorithms may use large index files to increase performance. Spaced seeds with large weights reduce the number of possible locations of a read within a reference sequence. Optimal patterns for spaced seeds may guarantee to align reads even with several substitutions.ResultsFor reads of 64–200 bp periodic spaced seeds of 32, 40, 48, 56, 64 weights are found that guarantee to locate all positions within a reference sequence for a specified number of point mutations. SIMD instructions to convert masked reads into 64, 80, 96, 112, 128-bit numbers are provided.AvailabilityC codes to generate spaced seeds and find optimal SIMD instructions for them are freely available under MIT license at https://github.com/vtman/VSTseed

This work introduces epanetCPA, an open-source MATLAB® toolbox for modelling the hydraulic response of water distribution systems to cyber-physical attacks. epanetCPA allows users to quickly design various attack scenarios and assess their impact via simulation with EPANET, a popular public-domain model for water network analysis. The toolbox offers both demand-driven and pressure-driven simulations, enabling the users to realistically analyze cyber-physical attacks and their impacts under both pressure sufficient and pressure deficient conditions. epanetCPA is available under the MIT license.

BackgroundThe interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently.ResultsWe developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility.ConclusionGeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both bird’s-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

SummaryCiliates are single-celled eukaryotes that eliminate specific, interspersed DNA sequences (internally eliminated sequences, IESs) from their genomes during development. These are challenging to annotate and assemble because IES-containing sequences are much less abundant in the cell than those without, and IES sequences themselves often contain repetitive and low-complexity sequences. Long read sequencing technologies from Pacific Biosciences and Oxford Nanopore have the potential to reconstruct longer IESs than has been possible with short reads, and also the ability to detect correlations of neighboring element elimination. Here we present BleTIES, a software toolkit for detecting, assembling, and analyzing IESs using mapped long reads.Availability and implementationBleTIES is implemented in Python 3. Source code is available at https://github.com/Swart-lab/bleties (MIT license), and also distributed via Bioconda.ContactContact: kb.seah@tuebingen.mpg.deSupplementary informationBenchmarking of BleTIES with published sequence data.

Summary bollito is an automated, flexible and parallelizable computational pipeline for the comprehensive analysis of single-cell RNA-seq data. Starting from FASTQ files or preprocessed expression matrices, bollito performs both basic and advanced tasks in single-cell analysis integrating >30 state-of-the-art tools. This includes quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory inference and RNA velocity. bollito is built using the Snakemake workflow management system, which easily connects each execution step and facilitates the reproducibility of results. bollito’s modular design makes it easy to incorporate other packages into the pipeline enabling its expansion with new functionalities. Availability and implementation Source code is freely available at https://gitlab.com/bu_cnio/bollito under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

Background Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings Herein, we present an extension of CNVnator developed in Python—CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number–neutral losses of heterozygosity. Conclusions CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2–20 times faster)—and has (20–50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

SAIGE-Biobank Re-Usable SAIGE Helper (SAIGE-BRUSH) allows users with little computational expertise to utilize SAIGE for GWAS with parallelization and data collection on biobank data sets. This implementation requires no installation and has additional features not programmed within the original SAIGE framework, such as concurrency, reproducibility, reusability, scalability, association analysis results filtering and output plots. This is all achieved without writing any code from the user. This implementation is currently being utilized by the Biobank at the Colorado Center for Personalized Medicine (CCPM) on Google Cloud but is flexible for a number of architectures available to genetic analysts. Availability: This open source implementation is freely available at https://github.com/tbrunetti/SAIGE-BRUSH and is licensed under the MIT License. Contact: Chris Gignoux at chris.gignoux@cuanschutz.edu & Nick Rafaels at nicholas.rafaels@cuanschutz.edu Supplemental Material: For detailed user documentation, please visit https://saige-brush.readthedocs.io/en/latest/

Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

SummaryEfficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe the new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model (ENM) for deformations along global modes, followed by clustering and short molecular dynamics (MD) simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.Availability and implementationClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy.Contactburak.kaynak@pitt.edu or doruker@pitt.eduSupplementary informationSupplementary materials comprise method details, figures, table and tutorial.

Purpose SpekPy is a free toolkit for modeling x-ray tube spectra with the Python programming language. In this article, the advances in version 2.0 (v2) of the software are described, including additional target materials and more accurate modeling of the heel effect. Use of the toolkit is also demonstrated. Methods The predictions of SpekPy are illustrated in comparison to experimentally determined spectra: three radiation quality reference (RQR) series tungsten spectra and one mammography spectrum with a molybdenum target. The capability of the software to correctly model changes in tube output with tube potential is also assessed, using the example of a GE RevolutionTM CT scanner (GE Healthcare, Waukesha, WI, USA) and specifications in the system's Technical Reference Manual. Furthermore, we note that there are several physics models available in SpekPy. These are compared on and off the central axis, to illustrate the differences. Results SpekPy agrees closely with the experimental spectra over a wide range of tube potentials, both visually and in terms of first and second half-value layers (HVLs) (within 2% here). The CT scanner spectrum output (normalized to 120 kV tube potential) agreed within 4% over the range of 70 to 140 kV. The default physics model (casim) is adequate in most situations. The advanced option (kqp) should be used if high accuracy is desired for modeling the anode heel effect, as it fully includes the effects of bremsstrahlung anisotropy. Conclusions SpekPy v2 can reliably predict on- and off-axis spectra for tungsten and molybdenum targets. SpekPy's open-source MIT license allows users the freedom to incorporate this powerful toolkit into their own projects.

Summary Digital pathology enables applying computational methods, such as deep learning, in pathology for improved diagnostics and prognostics, but lack of interoperability between whole slide image formats of different scanner vendors is a challenge for algorithm developers. We present OpenPhi—Open PatHology Interface, an Application Programming Interface for seamless access to the iSyntax format used by the Philips Ultra Fast Scanner, the first digital pathology scanner approved by the United States Food and Drug Administration. OpenPhi is extensible and easily interfaced with existing vendor-neutral applications. Availability and implementation OpenPhi is implemented in Python and is available as open-source under the MIT license at: https://gitlab.com/BioimageInformaticsGroup/openphi. The Philips Software Development Kit is required and available at: https://www.openpathology.philips.com. OpenPhi version 1.1.1 is additionally provided as Supplementary Data. Supplementary information Supplementary data are available at Bioinformatics online.

Fast-scan cyclic voltammetry at carbon fiber microelectrodes measures low concentrations of analytes in biological systems. There are ongoing efforts to simplify FSCV analysis and several custom platforms are available for filtering and multi-modal analysis of FSCV signals but there is no single, easily accessible platform that has capacity for all these features. Here we present The Analysis Kid: a free, open-source cloud application that does not require a specialized runtime environment and is easily accessible via common browsers. We show that a user-friendly interface can analyze multi-platform file formats to provide multimodal visualization of FSCV color plots with digital background subtraction. We highlight key features that allow interactive calibration and parametric analysis via peak finding algorithms to automatically detect the maximum amplitude, area under the curve and clearance rate of the signal. Finally, The Analysis Kid enables semi-automatic fitting of data with Michaelis Menten kinetics with single or dual reuptake models. The Analysis Kid can be freely accessed at https://analysis-kid.herokuapp.com/. The web application code is found, under an MIT license, at https://github.com/sermeor/The-Analysis-Kid.

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson’s Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license., Author summary Parkinson’s Disease is the fastest growing neurological condition affecting millions of people across the world. People with Parkinson’s suffer from a variety of symptoms that result in diminished ability to move, eat, remember or sleep. Research in new treatments are limited because the clinical tools used to assess its symptoms are subjective, require considerable time to perform and specialised skills and can only detect coarse-grain changes. To address this situation, clinicians are turning to smartphone apps and wearables to create new ways to assess symptoms that are more sensitive to change and can be applied frequently at home by patients and their carers. In this paper, we discuss PDkit, an open source toolkit that we developed to help address this current lack of algorithmic and model transparency. Adopting PDkit facilitates the open sharing of standardised methods and can accelerate the development of new methods and system to assess Parkinson’s and enables research groups to innovate. The toolkit provides funcionality that support data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. The practical use of PDkit is demonstrated via its use by the CUSSP clinical trial conducted in the UK.