GNU General Public License V3 Research Articles

Olsson.wl & ROC2.wl: Mathematica packages for transformations of multivariable hypergeometric functions & regions of convergence for their series representations in the two variables case

We present the Olsson.wlMathematica package which aims to find transformations for some classes of multivariable hypergeometric functions. It is based on a well-known method developed by P. O. M. Olsson [1] (1964) to derive the analytic continuations of the Appell F1 double hypergeometric series using the linear transformations of the Gauss F12 hypergeometric function. We provide a brief description of the method of Olsson and demonstrate the use of the commands of the Olsson.wl package using some examples that are presented in the text and in some ancillary Mathematica notebooks. In particular, we reproduce various results of the literature on multivariable hypergeometric functions and show practical applications of this package in the derivation of novel formulas. In the context of high energy physics, we also demonstrate how it can be used to disentangle some known results about the analytic continuation of some series representations of the one-loop pentagon in multi-Regge kinematics and D=6−2ϵ. We also provide a companion package, called ROC2.wl, which is dedicated to the derivation of the regions of convergence of double hypergeometric series. This package can be used independently of Olsson.wl. Program summaryProgram Title:Olsson.wlCPC Library link to program files:https://doi.org/10.17632/gc63xzwzz5.1Licensing provisions: GNU General Public License v3.0.Programming language: Wolfram Mathematica version 11.3 and beyond.Nature of problem: To find the transformation formulas of multivariable hypergeometric series appearing in Feynman integral calculus.Solution method: Mathematica implementation of the method of Olsson [1]. The method uses the transformation theory of lower variable hypergeometric functions to find the transformation formulas of higher variable hypergeometric functions.Companion package:ROC2.wl, which is a Mathematica package used inside Olsson.wl and can also be used as a standalone package. The package is used to find the region of convergence of double hypergeometric functions, using Horn's theorem.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts.

Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

SMIwiz: An integrated toolbox for multidimensional seismic modelling and imaging

This paper contributes an open source software - SMIwiz, which integrates seismic modelling, reverse time migration (RTM), and full waveform inversion (FWI) into a unified computer implementation. SMIwiz has the machinery to do both 2D and 3D simulation in a consistent manner. The package features a number of computational recipes for efficient calculation of imaging condition and inversion gradient: a dynamic evolving computing box to limit the simulation cube and a well-designed wavefield reconstruction strategy to reduce the memory consumption when dealing with 3D problems. The modelling in SMIwiz runs independently: each shot corresponds to one processor in a bijective manner to maximize the scalability. A batchwise job scheduling strategy is designed to handle large 3D imaging tasks on computer with limited number of cores. The viability of SMIwiz is demonstrated by a number of applications on benchmark models. Program summaryProgram Title: SMIwizCPC Library link to program files:https://doi.org/10.17632/tygszns27k.1Developer's repository link:https://github.com/yangpl/SMIwizLicensing provisions: GNU General Public License v3.0Programming language: C, Shell, FortranExternal dependencies: MPI [1], FFTW [2]Nature of problem: Seismic modelling and imaging (FWI and RTM)Solution method: High-order finite-difference time-domain (FDTD) for modelling on staggered grid; Quasi-Newton LBFGS algorithm for nonlinear optimization; line search to estimate step length based on Wolfe condition References[1]https://www.mpich.org/[2]http://fftw.org/

MoULDyS: Monitoring of autonomous systems in the presence of uncertainties

We introduce MoULDyS, that implements efficient offline and online monitoring algorithms of black-box cyber-physical systems w.r.t. safety properties. MoULDyS takes as input an uncertain log (with noisy and missing samples), as well as a bounding model in the form of an uncertain linear system; this latter model plays the role of an over-approximation so as to reduce the number of false alarms. MoULDyS is Python-based and available under the GNU General Public License v3.0 (gpl-3.0). We further provide easy-to-use scripts to recreate the results of two case studies introduced in an earlier work.

GRETTA: an R package for mapping in silico genetic interaction and essentiality networks.

Mapping genetic interactions and essentiality networks in human cell lines has been used to identify vulnerabilities of cells carrying specific genetic alterations and to associate novel functions to genes, respectively. In vitro and in vivo genetic screens to decipher these networks are resource-intensive, limiting the throughput of samples that can be analyzed. In this application note, we provide an R package we call Genetic inteRaction and EssenTiality neTwork mApper (GRETTA). GRETTA is an accessible tool for in silico genetic interaction screens and essentiality network analyses using publicly available data, requiring only basic R programming knowledge. The R package, GRETTA, is licensed under GNU General Public License v3.0 and freely available at https://github.com/ytakemon/GRETTA and https://doi.org/10.5281/zenodo.6940757, with documentation and tutorial. A Singularity container is also available at https://cloud.sylabs.io/library/ytakemon/gretta/gretta.

SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis

BackgroundCurrent proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue.ResultsWe developed a state-of-the-art toolkit (SeqWiz) for constructing next-generation sequence databases and performing proteomic-centric sequence analyses. First, we proposed two derived data formats: SQPD (a well-structured and high-performance local sequence database based on SQLite), and SET (an associated list of selected entries based on JSON). The SQPD format follows the basic standards of the emerging PEFF format, which also aims to facilitate the search of complex proteoform. The SET format is designed for generating subsets with with high-efficiency. These formats are shown to greatly outperform the conventional FASTA or PEFF formats in time and resource consumption. Then, we mainly focused on the UniProt knowledgebase and developed a collection of open-source tools and basic modules for retrieving species-specific databases, formats conversion, sequence generation, sequence filter, and sequence analysis. These tools are implemented by using the Python language and licensed under the GNU General Public Licence V3. The source codes and distributions are freely available at GitHub (https://github.com/fountao/protwiz/tree/main/seqwiz).ConclusionsSeqWiz is designed to be a collection of modularized tools, which is friendly to both end-users for preparing easy-to-use sequence databases as well as bioinformaticians for performing downstream sequence analysis. Besides the novel formats, it also provides compatible functions for handling the traditional text based FASTA or PEFF formats. We believe that SeqWiz will promote the implementing of complementary proteomics for data renewal and proteoform analysis to achieve precision proteomics. Additionally, it can also drive the improvement of proteomic standardization and the development of next-generation proteomic softwares.

LibEMM: A fictious wave domain 3D CSEM modelling library bridging sequential and parallel GPU implementation

This paper delivers a software - libEMM - for 3D controlled-source electromagnetics (CSEM) modelling in fictitious wave domain, based on the newly developed high-order finite-difference time-domain (FDTD) method on non-uniform grid. The numerical simulation can be carried out over a number of parallel processors using MPI-based high performance computing architecture. The FDTD kernel coded in C has been parallelized with OpenMP for speedup using local shared memory. In addition, the software features a GPU implementation of the same algorithm based on CUDA programming language, which can be cross-validated and compared in terms of efficiency. A perspective of libEMM on the horizon is its application to 3D CSEM inversion in land and marine environment. Program summaryProgram Title: libEMMCPC Library link to program files:https://doi.org/10.17632/p769t7c5bk.1Developer's repository link:https://github.com/yangpl/libEMMLicensing provisions: GNU General Public License v3.0Programming language: C, CUDA, Fortran, ShellExternal dependencies: MPI [1], FFTW [2], CUDA [3]Nature of problem: Controlled-source electromagnetics (CSEM)Solution method: High-order finite-difference time-domain (FDTD) on non-uniform grid by fictious wave domain transformation References[1]https://www.mpich.org/[2]http://fftw.org/[3]https://developer.nvidia.com/cuda-toolkit

ASSIST: An Ephemeris-quality Test-particle Integrator

We introduce ASSIST, a software package for ephemeris-quality integrations of test particles. ASSIST is an extension of the REBOUND framework and makes use of its IAS15 integrator to integrate test-particle trajectories in the field of the Sun, Moon, planets, and 16 massive asteroids, with the positions of the masses coming from the Jet Propulsion Laboratory’s DE441 ephemeris and its associated asteroid perturber file. The package incorporates the most significant gravitational harmonics and general-relativistic corrections. ASSIST also accounts for position- and velocity-dependent nongravitational effects. The first-order variational equations are included for all terms to support orbit fitting and covariance mapping. This new framework is meant to provide an open-source package written in a modern language to enable high-precision orbital analysis and science by the small-body community. ASSIST is open source, freely distributed under the GNU General Public license v3.0.

DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies.

DeepPheWAS is an R package for phenome wide association studies that creates clinically-curated composite phenotypes, and integrates quantitative phenotypes from primary care data, longitudinal trajectories of quantitative measures, disease progression, and drug response phenotypes. Tools are provided for efficient analysis of association with any genetic input, under any genetic model, with optional sex-stratified analysis, and for developing novel phenotypes. The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at https://github.com/Richard-Packer/DeepPheWAS. Supplementary methods and results are available at Bioinformatics online.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding.

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

Screenwerk: A modular tool for the design and analysis of drug combination screens.

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. screenwerk is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens. The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy; Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no. Supplementary data are available at Bioinformatics online.

GTDB-Tk v2: memory friendly classification with the genome taxonomy database.

The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification. GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk. Supplementary data are available at Bioinformatics online.

MUSE: An open-source agent-based integrated assessment modelling framework

Integrated assessment models (IAMs) are a cornerstone of an effective approach to climate change mitigation. Despite the variety of methodologies for characterising the energy system, land use change, economics, and climate response, the modelling community has an open and urgent request for tools capable of more realistic interpretation of the energy transition, capturing human behaviour, and embodying the principles of transparency, reproducibility, and flexibility of use.This paper presents an open-source modelling framework designed to fill that gap. Named MUSE (ModUlar energy systems Simulation Environment), this new agent-based model supports flexible characterisation of agent decision-making, including individual goals, bounded-rationality, imperfect foresight, and limited knowledge during the decision process. MUSE integrates this agent-based approach in a partial-equilibrium framework and enables a technology-rich description of the energy systems with an unprecedented degree of flexibility for including technological, temporal, and geographical granularity. The structure of MUSE creates the ability to produce climate change mitigation assessments that are more grounded, and more tangible model outputs for conceiving effective approaches to mitigation. MUSE is available open source under a GNU General Public License v3.0 on GitHub at this link https://github.com/SGIModel/MUSE_OS.

PyThea: An open-source software package to perform 3D reconstruction of coronal mass ejections and shock waves

PyThea is a newly developed open-source Python software package that provides tools to reconstruct coronal mass ejections (CMEs) and shocks waves in three dimensions, using multi-spacecraft remote-sensing observations. In this article, we introduce PyThea to the scientific community and provide an overview of the main functionality of the core software package and the web application. This package has been fully built in Python, with extensive use of libraries available within this language ecosystem. PyThea package provides a web application that can be used to reconstruct CMEs and shock waves. The application automatically retrieves and processes remote-sensing observations, and visualizes the imaging data that can be used for the analysis. Thanks to PyThea, the three-dimensional reconstruction of CMEs and shock waves is an easy task, with final products ready for publication. The package provides three widely used geometrical models for the reconstruction of CMEs and shocks, namely, the graduated cylindrical shell (GCS) and an ellipsoid/spheroid model. It also provides tools to process the final fittings and calculate the kinematics. The final fitting products can also be exported and reused at any time. The source code of PyThea package can be found in GitHub and Zenodo under the GNU General Public License v3.0. In this article, we present details for PyThea‘s python package structure and its core functionality, and we show how this can be used to perform three-dimensional reconstruction of coronal mass ejections and shock waves.

Express: Extensible, high-level workflows for swifter ab initio materials modeling

In this work, we introduce an open-source Julia project, express, an extensible, lightweight, high-throughput, high-level workflow framework that aims to automate ab initio calculations for the materials science community. express is shipped with well-tested workflow templates, including structure optimization, equation of state (EOS) fitting, phonon spectrum (lattice dynamics) calculation, and thermodynamic property calculation in the framework of the quasi-harmonic approximation (QHA). It is designed to be highly modularized so that its components can be reused across various occasions, and customized workflows can be built on top of that. Users can also track the status of workflows in real-time, and rerun failed jobs thanks to the data lineage feature express provides. Two working examples, i.e., all workflows applied to lime and akimotoite, are also presented in the code and this paper. Program summaryProgram Title:expressCPC Library link to program files:https://doi.org/10.17632/bbzzrxsm4b.1Developer's repository link:https://github.com/MineralsCloud/Express.jlLicensing provisions: GNU General Public License v3.0Programming language:JuliaNature of problem: High-performance ab initio calculation is gaining more and more popularity when investigating the physical and chemical properties of materials in the scientific community. There are several ab initio programs in the market, but they are often not user-friendly to new users because of their intrinsic complexity. Even for familiar users, dealing with daily preparation and post-analysis could be trivial and fallible. Therefore, plenty of workflow frameworks have been developed to solve this problem. However, most of them cannot meet our expectations for several reasons. They are either too complex to read and modify, or too heavy for simple tasks. Plus, they put little focus on addressing the real pain points of everyday calculations, such as data preparation and output parsing.Solution method: We developed a workflow framework that can simplify this process, i.e., most of the mundane work can be replaced by writing a few lines of configurations. We also automated the three most-used work procedures into configurable workflows: equations of state fitting (structural optimization), phonon spectrum calculation, and thermodynamics calculation.

Expam-high-resolution analysis of metagenomes using distance trees.

SummaryShotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing.Availability and implementation expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam.Supplementary information Supplementary data are available at Bioinformatics online.

Beacon v2 Reference Implementation: a toolkit to enable federated sharing of genomic and phenotypic data.

Beacon v2 is an API specification established by the Global Alliance for Genomics and Health initiative (GA4GH) that defines a standard for federated discovery of genomic and phenotypic data. Here, we present the Beacon v2 Reference Implementation (B2RI), a set of open-source software tools that allow lighting up a local Beacon instance 'out-of-the-box'. Along with the software, we have created detailed 'Read the Docs' documentation that includes information on deployment and installation. The B2RI is released under GNU General Public License v3.0 and Apache License v2.0. Documentation and source code is available at: https://b2ri-documentation.readthedocs.io. Supplementary data are available at Bioinformatics online.

Data-analysis software framework 2DMAT and its application to experimental measurements for two-dimensional material structures

An open-source data-analysis framework 2DMAT has been developed for experimental measurements of two-dimensional material structures. 2DMAT offers five analysis methods: (i) Nelder-Mead optimization, (ii) grid search, (iii) Bayesian optimization, (iv) replica exchange Monte Carlo method, and (v) population-annealing Monte Carlo method. Methods (ii) through (v) are implemented by parallel computation, which is efficient not only for personal computers but also for supercomputers. The current version of 2DMAT is applicable to total-reflection high-energy positron diffraction (TRHEPD), surface X-ray diffraction (SXRD), and low-energy electron diffraction (LEED) experiments by installing corresponding forward problem solvers that generate diffraction intensity data from a given dataset of the atomic positions. The analysis methods are general and can be applied also to other experiments and problems. Program summaryProgram Title: 2DMATCPC Library link to program files:https://doi.org/10.17632/c2t3vzbx9f.1Developer's repository link:https://www.pasums.issp.u-tokyo.ac.jp/2dmat/Code Ocean capsule:https://codeocean.com/capsule/7260490Licensing provisions: GNU General Public License v3.0Programming language: Python 3External routines/libraries: Numpy, Scipy, Tomli, mpy4pyNature of problem: Analysis of experimental measurement data.Solution method: Optimization, grid-based global search, Monte Carlo method.

PrISM: precision for integrative structural models.

A single-precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. We develop PrISM (Precision for Integrative Structural Models) to efficiently identify high- and low-precision regions for integrative models. PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper are available at doi:10.5281/zenodo.6241200. Supplementary data are available at Bioinformatics online.