GNU General Public License V3 Research Articles

SternheimerGW: A program for calculating [formula omitted] quasiparticle band structures and spectral functions without unoccupied states

The SternheimerGW software uses time-dependent density-functional perturbation theory to evaluate GW quasiparticle band structures and spectral functions for solids. Both the Green’s function G and the screened Coulomb interaction W are obtained by solving linear Sternheimer equations, thus overcoming the need for a summation over unoccupied states. The code targets the calculation of accurate spectral properties by convoluting G and W using a full frequency integration. The linear response approach allows users to evaluate the spectral function at arbitrary electron wavevectors, which is particularly useful for indirect band gap semiconductors and for simulations of angle-resolved photoelectron spectra. The software is parallelized efficiently, integrates with version 6.3 of Quantum Espresso, and is continuously monitored for stability using a test farm. Program summaryProgram Title:SternheimerGWProgram Files doi:http://dx.doi.org/10.17632/7ywzjng9t5.1Licensing provisions: GNU General Public License v3.0Programming language: Fortran 2003Nature of problem: The lack of the exchange–correlation discontinuity in density-functional theory (DFT) leads to a systematic underestimation of the band gap between conduction and valence states. Many-body perturbation theory in the GW approximation provides an effective solution to this problem, as well as other limitations faced by DFT in the description of electronic excitations. However, the GW method comes with its own set of limitations: (i) The perturbation of the system is typically expressed in terms of the unoccupied states, and achieving numerical convergence with respect to these states is often cumbersome. Since the underlying DFT codes rely only on the occupied states, their default behavior is often ill-suited to provide a sufficient amount of empty states. Combined with the large number of parameters that need to be converged, this limits the use of GW codes by non-expert users and automatic scripts. (ii) Currently, GW codes require that a homogeneous k-point mesh is used in the calculation. Hence, features close to the band edges are often only accessible via prohibitively expensive dense k-point meshes or interpolation techniques. (iii) To evaluate the frequency convolution of the Green’s function G and the screened Coulomb interaction W, many current GW calculations rely on approximations such as the plasmon-pole approximation, the analytic continuation, or the contour deformation. The relative merits and accuracy of the various approximations are not fully understood.Solution method: In SternheimerGW, we address (i) by replacing the summation over unoccupied states with a linear response equation. The solution depends on the occupied states only, and it employs linear response algorithms already provided by the Quantum ESPRESSO suite to compute phonons and related properties. As an additional benefit, transforming the problem in a linear response equation removes the restriction to homogeneous k-point meshes (ii), so that the GW self-energy for any arbitrary point can be evaluated. Finally (iii), we provide the possibility to perform a full frequency convolution along the real frequency axis. This feature can serve as a benchmark for approximate integrations using models or analytic continuation.

BA3‐SNPs: Contemporary migration reconfigured in BayesAss for next‐generation sequence data

Abstract Quantifying “demographic independence” is a vital step in establishing potential conservation units for a species in that it effectively distinguishes migration from within‐population reproduction. This is an important aspect because it allows for an accurate estimate of recruitment. For example, populations may be designated as 'management units' (=MUs) if indeed population growth results from local demography rather than immigration. Of additional interest is the calculation of immigrant ancestry and ascertainment of the temporal context over which immigration occurred. This is because MUs depend largely upon local (self‐sustaining) birth and death rates, and the quantification of ancestry is necessary to validate demographic independence. Dispersal rate is also of immediate interest to conservation biologists, and can be assessed by quantifying genetic divergence among populations. The capacity with which to gauge these benchmarks has now been extended herein to genome‐wide molecular data, in an attempt to adjust an analytical tool that was until now intractable for the next generation sequencing data. In this study, a popular legacy program for migrant detection (i.e. BayesAss3) has been modified to accept SNP (single nucleotide polymorphism) data. We validated BA3‐SNPs using empirical data to demonstrate its suitability for both high‐performance and desktop computing environments. We also facilitate high analytical throughput by presenting a binary search algorithm that automates MCMC (Markov chain Monte Carlo) parameter tuning. Our BA3‐SNPs‐autotune program required five or fewer rounds of optimization for 99% of input files, with acceptable mixing parameters derived in 100% of our test cases. Runtime for BA3‐SNPs is a function of the number of loci analysed. Benchmarking yielded an average runtime <32 hr (10 million MCMC generations) for datasets containing thousands of SNPs. The BA3 algorithm remains a viable option for analysing modern SNP datasets. Source code (C++ and Python) is released publicly under the GNU General Public License v3.0, and is available for download (Linux and Mac OSX) from the following URL: https://github.com/stevemussmann/.

Hicap: In Silico Serotyping of the Haemophilus influenzae Capsule Locus.

Haemophilus influenzae exclusively colonizes the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases, including meningitis and sepsis. A key virulence determinant of H. influenzae is the polysaccharide capsule, of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a to cap-f). H. influenzae type b (Hib) was historically responsible for the majority of invasive H. influenzae disease, and its prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, nontypeable H. influenzae emerged as the most dominant group causing disease, but in recent years a resurgence of encapsulated H. influenzae strains has also been observed, most notably serotype a. Given the increasing incidence of encapsulated strains and the high frequency of Hib in countries without vaccination programs, there is growing interest in genomic epidemiology of H. influenzae Here we present hicap, a software tool for rapid in silico serotype prediction from H. influenzae genome sequences. hicap is written using Python3 and is freely available at https://github.com/scwatts/hicap under the GNU General Public License v3 (GPL3). To demonstrate the utility of hicap, we used it to investigate the cap locus diversity and distribution in 691 high-quality H. influenzae genomes from GenBank. These analyses identified cap loci in 95 genomes and confirmed the general association of each serotype with a unique clonal lineage, and they also identified occasional recombination between lineages that gave rise to hybrid cap loci (2% of encapsulated strains).

Программа «Moments of inertia» для расчёта моментов инерции вращательного движения молекул

The moment of inertia of the rotational motion I, as a descriptor of the spatial structure of the molecule, which determines the properties of a substance, in accordance with the works of recent years, begins to acquire significance in the study of the «structure – property» dependencies, allowing one to describe the change in the properties of compounds in homologous series and address odd homologues. The problem is that there is no universal and transparent method for calculating the moments of inertia of the rotational motion of molecules. Researchers are trying to solve this problem in various ways: presenting the molecule as its carbon chain and calculating the moments of inertia only for it, neglecting the contribution of other atoms, manually calculating the moments of inertia for small simple molecules, based on their estimated geometry, extracting intermediate results from quantum chemical calculations of the program packages like Gaussian or Gamess. We have developed a program for the exact calculation of the moments of inertia, which uses the specification of the exact geometry of the molecule in three-dimensional space using Cartesian coordinates. The program is written on Perl programming language and is available under the GNU General Public License v3.0 (free software). The program uses XYZ files as input data. The principle of the program is to iteratively calculate the inertia moments for all possible positions in the space of the axis of rotation passing through the center of mass of the calculated molecule. The minimum and maximum values of the moments of inertia obtained during the calculation correspond to two perpendicular axes of rotation of the molecule (x and z). The moment of inertia with respect to the third remaining y axis is calculated after finding the canonical equation of the straight axis perpendicular to the found x and z axes.

EpiLog: A software for the logical modelling of epithelial dynamics.

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines.

SummaryTranslational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones.Availability and implementationFORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3.Supplementary information Supplementary data are available at Bioinformatics online.

EpiLog: A software for the logical modelling of epithelial dynamics

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

Comp-D: a program for comprehensive computation of D-statistics and population summaries of reticulated evolution

Computation of Patterson’s D-statistic and its five-taxon derivatives are important phylogenetic methods for the quantification of reticulated evolution, yet are limited in application by the lack of a single, comprehensive program to efficiently perform all necessary calculations from common phylogenetic and population genetic program file formats. To increase accessibility for a broad range of researchers, we present a user-friendly program (COMP-D) that provides flexibility for incorporating heterozygous sites, implements multiple statistical methods, and aggregates results from multiple tests. Program augmentations also facilitate the detection of population-level introgression. COMP-D provides a threefold increase in speed relative to comparable software. It is implemented in C++ and released under the GNU General Public License v3.0. Source code is available for Linux/Mac OS X from: https://github.com/stevemussmann/Comp-D_MPI.

NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

AbstractNIFTy, “Numerical Information Field Theory,” is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high‐dimensional real‐world problems. This paper introduces NIFTy 3, a major upgrade to the original NIFTy framework. NIFTy 3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy 3 is able to handle non‐scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real‐world algorithm from the realm of information field theory. NIFTy 3 is open‐source software available under the GNU General Public License v3 (GPL‐3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/NIFTy_3.

PolyDFEv2.0: testing for invariance of the distribution of fitness effects within and across species.

Distribution of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for distinct sources of heterogeneity (chance and genuine difference in DFE) underlying differences in SFS data in these regions. Not accounting for this will result in the spurious detection of DFE differences. polyDFEv2.0 is implemented in C and is accompanied by a series of R functions that facilitate post-processing of the output. It is available as source code and compiled binaries under a GNU General Public License v3.0 from https://github.com/paula-tataru/polyDFE. Supplementary data are available at Bioinformatics online.

Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT).

The ability to measure molecular properties (e.g., mRNA expression) at the single-cell level is revolutionizing our understanding of cellular developmental processes and how these are altered in diseases like cancer. The need for computational methods aimed at extracting biological knowledge from such single-cell data has never been greater. Here, we present a detailed protocol for estimating differentiation potency of single cells, based on our Single-Cell ENTropy (SCENT) algorithm. The estimation of differentiation potency is based on an explicit biophysical model that integrates the RNA-Seq profile of a single cell with an interaction network to approximate potency as the entropy of a diffusion process on the network. We here focus on the implementation, providing a step-by-step introduction to the method and illustrating it on a real scRNA-Seq dataset profiling human embryonic stem cells and multipotent progenitors representing the 3 main germ layers. SCENT is aimed particularly at single-cell studies trying to identify novel stem-or-progenitor like phenotypes, and may be particularly valuable for the unbiased identification of cancer stem cells. SCENT is implemented in R, licensed under the GNU General Public Licence v3, and freely available from https://github.com/aet21/SCENT .

High Performance Quantification of Complex High Resolution Polymer Mass Spectra.

Modern soft ionization mass spectrometry provides chemical information on various polymers with unparalleled resolution and sensitivity. However, the interpretation of the resulting highly complex mass spectra is hampered by the sheer amount of contributing macromolecular species. For example, state-of-the-art reversible deactivation radical polymerization techniques, which are generally considered to be highly controlled, can still generate tens or even hundreds of species in a narrow mass window. Moreover, the multitude of species typically leads to partially overlapping isotopic patterns, further complicating the data evaluation. Herein, a rapid and powerful three-step methodical approach is introduced that enables the successful identification and quantification of the contributing species. The approach is subsequently implemented in "pyMacroMS", a high performance algorithm that allows for ultrafast processing of high resolution polymer mass spectra with varying complexities. The power of our algorithm is demonstrated on the example of a photochemical atom transfer radical polymerization (photoATRP) of three monomers, ultimately leading to 908 identified species. pyMacroMS is available free of charge under a GNU General Public License v3.0.

An IMU-based traffic and road condition monitoring system

Abstract This paper presents a new type of wireless platform designed for real-time traffic estimation and road surface monitoring. The sensor platform is built around a 32-bit ARM Cortex M4 micro-controller and a LSM9DS0 Inertial Measurement Unit module. This platform is mainly designed for probe vehicles and can be easily installed in a vehicle equipped with a USB charger. The hardware architecture design and software programming system of the proposed platform are introduced as well as its cost evaluation from the first generation to the third generation. The article then demonstrates some applications of such a platform in smart cities, including trajectory estimation and road condition monitoring. All design files have been uploaded and shared in an open science framework, and can be accessed from https://osf.io/524y9/?view_only=85859a345a7b429fbb8fe194966daa5b . It is licensed under the GNU General Public License v3.0.

EpiLog: A software for the logical modelling of epithelial dynamics.

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

Porcupine: A visual pipeline tool for neuroimaging analysis.

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs one’s analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing one’s analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. Our goal is to release researchers from the constraints of specific implementation details, thereby freeing them to think about novel and creative ways to solve a given problem. Porcupine improves the overview researchers have of their processing pipelines, and facilitates both the development and communication of their work. This will reduce the threshold at which less expert users can generate reusable pipelines. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and make it easier for researchers to create reproducible and shareable science. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, and released under the GNU General Public License v3.0.

InDEx: Open Source iOS and Android Software for Self-Reporting and Monitoring of Alcohol Consumption.

InDEx is a software package for reporting and monitoring alcohol consumption via a smartphone application. Consumption of alcohol is self-reported by the user, and the app provides a visual representation of drinking behaviour and offers feedback on consumption levels compared to the general population. InDEx is intended as an exemplar app, operating as a standalone smartphone application and is highly customisable for a variety of research domains. InDEx is written in JavaScript, using IONIC framework which is cross-platform and is available under the liberal GNU General Public License (v3). The software is available from GitHub (https://github.com/DrDanL/index-app-public).

CrossPlan: systematic planning of genetic crosses to validate mathematical models.

Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test. We present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. We prove that the CrossPlan problem is NP-complete. We develop an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. We also extend our solution to incorporate other experimental conditions such as a delay factor that decides the availability of a mutant and genetic markers to confirm gene deletions. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence, our framework should be relevant in plant and animal systems as well. CrossPlan code is freely available under GNU General Public Licence v3.0 at https://github.com/Murali-group/crossplan. Supplementary data are available at Bioinformatics online.

Single-Cell Transcriptome Analysis Using SINCERA Pipeline.

Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html .

GEOMetaCuration: a web-based application for accurate manual curation of Gene Expression Omnibus metadata.

Metadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org.Database URL: https://bitbucket.com/yubiolab/GEOMetaCuration

Multi-hop communication in the uplink for LPWANs

Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOX™or LoRaWAN™rely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the Distance-Ring Exponential Stations Generator (DRESG) framework,11All of the source code in DRESG framework for LPWANs is open [1], encouraging sharing of algorithms between contributors and providing the ability for people to improve on the work of others under the GNU General Public License v3.0. designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.