GNU GPL Research Articles

OpenFMR: An open-source broadband ferromagnetic resonance spectrometer.

We describe a broadband ferromagnetic resonance spectrometer for scientific and educational applications with a frequency range of up to 30GHz. It is built with components available off-the-shelf, utilizes 3D printed parts for sample holders and support structures, and requires little assembly. A PCB design for the grounded coplanar waveguide (GCPW) is presented and analyzed. We further include a software suite for command-line or script driven data acquisition, a graphical user interface, and a graphical data analysis program. The capabilities of the system design are demonstrated with measurements on ferromagnetic thin films with a thickness of 1nm. All designs and scripts are published under the GNU GPL v3.0 license.

StreamChol: a web-based application for predicting cholestasis

This article introduces StreamChol, a software for developing and applying mechanistic models to predict cholestasis. StreamChol is a Streamlit application, usable as a desktop application or web-accessible software when installed on a server using a docker container.StreamChol allows a seamless integration of pharmacokinetic analyses with Machine Learning models. This integration not only enables cholestasis prediction but also opens avenues for predicting other toxicological endpoints requiring similar integrations. StreamChol's Docker containerization also streamlines deployment across diverse environments, addressing potential compatibility issues. StreamChol is distributed as open-source under GNU GPL v3, reflecting our commitment to open science. Through StreamChol, researchers are offered a potent tool for predictive modelling in toxicology, harnessing its strengths within an intuitive and user-friendly interface, without the need for any programming knowledge.Scientific contribution This work offers a user-friendly web-based tool for cholestasis prediction and a complete workflow for creating web platforms that require the combination of both programming languages, R and Python.

Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes.

Gene function annotation in microbial genomes and metagenomes is a fundamental in silico first step toward understanding metabolic potential and determinants of fitness. The Kyoto Encyclopedia of Genes and Genomes publishes a curated list of profile hidden Markov models to identify orthologous gene families (KOfams) with roles in metabolism. However, the computational tools that rely upon KOfams yield different annotations for the same set of genomes, leading to different downstream biological inferences. Here, we apply three open-source software tools that can annotate KOfams to genomes of phylogenetically diverse bacterial families from host-associated and free-living biomes. We use multiple computational approaches to benchmark these methods and investigate individual case studies where they differ. Our results show that despite their fundamental similarities, these methods have different annotation rates and quality. In particular, a method that adaptively tunes sequence similarity thresholds substantially improves sensitivity while maintaining high accuracy. We observe particularly large improvements for protein families with few reference sequences, or when annotating genomes from nonmodel organisms (such as gut-dwelling Lachnospiraceae). Our findings show that small improvements in annotation workflows can maximize the utility of existing databases and meaningfully improve in silico characterizations of microbial metabolism. Anvi'o is available at https://anvio.org under the GNU GPL license. Scripts and workflow are available at https://github.com/pbradleylab/2023-anvio-comparison under the MIT license.

Predicting Phylogenetic Bootstrap Values via Machine Learning.

Estimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The still most widely used method for calculating branch support on trees inferred under maximum likelihood (ML) is the Standard, nonparametric Felsenstein bootstrap support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the rapid bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira-Hasegawa-like approximate likelihood ratio test) or the UltraFast bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or unstable behavior in the low support interval range (SH-aLRT). Here, we present the educated bootstrap guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 (σ=5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can, for instance, predict SBS support values on a phylogeny comprising 1,654 SARS-CoV2 genome sequences within 3 h on a mid-class laptop. EBG is available under GNU GPL3.

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics.

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss.

Genomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree. We present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic-reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as duplication, transfer, and loss probabilities for genome-scale datasets with hundreds of taxa. GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz.

Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.

MQCT 2024: A program for calculations of inelastic scattering of two molecules (new version announcement)

MQCT 2024: A program for calculations of inelastic scattering of two molecules (new version announcement)

SCORPIO: a utility for defining and classifying mutation constellations of virus genomes.

Scorpio provides a set of command line utilities for classifying, haplotyping, and defining constellations of mutations for an aligned set of genome sequences. It was developed to enable exploration and classification of variants of concern within the SARS-CoV-2 pandemic, but can be applied more generally to other species. Scorpio is an open-source project distributed under the GNU GPL version 3 license. Source code and binaries are available at https://github.com/cov-lineages/scorpio, and binaries are also available from Bioconda. SARS-CoV-2 specific definitions can be installed as a separate dependency from https://github.com/cov-lineages/constellations.

Extending StringSpinner to handle vector-meson spin

Extending StringSpinner to handle vector-meson spin

MOSTO: A toolkit to facilitate security auditing of ICS devices using Modbus/TCP

MOSTO: A toolkit to facilitate security auditing of ICS devices using Modbus/TCP

ROMR: A ROS-based open-source mobile robot

ROMR: A ROS-based open-source mobile robot

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging. We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and dx.doi.org/10.17504/protocols.io.8epv56e74g1b/v1. Supplementary data are available at Bioinformatics online.

TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches

Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO.

The VM2D Open Source Code for Two-Dimensional Incompressible Flow Simulation by Using Fully Lagrangian Vortex Particle Methods

This article describes the open-source C++ code VM2D for the simulation of two-dimensional viscous incompressible flows and solving fluid-structure interaction problems. The code is based on the Viscous Vortex Domains (VVD) method developed by Prof. G. Ya. Dynnikova, where the viscosity influence is taken into account by introducing the diffusive velocity. The original VVD method was supplemented by the author’s algorithms for boundary condition satisfaction, which made it possible to increase the accuracy of flow simulation near the airfoil’s surface line and reduce oscillations when calculating hydrodynamic loads. This paper is aimed primarily at assessing the efficiency of the parallelization of the algorithm. OpenMP, MPI, and Nvidia CUDA parallel programming technologies are used in VM2D, which allow performing simulations on computer systems of various architectures, including those equipped with graphics accelerators. Since the VVD method belongs to the particle methods, the efficiency of parallelization with the usage of graphics accelerators turns out to be quite high. It is shown that in a real simulation, one graphics card can replace about 80 nodes, each of which is equipped with 28 CPU cores. The source code of VM2D is available on GitHub under GNU GPL license.

FastContext: A tool for identification of adapters and other sequence patterns in next generation sequencing (NGS) data.

The development of next generation sequencing (NGS) methods has created the need for detailed analysis and control of each protocol step. NGS library preparation protocols may include steps with incorporation of various service sequences, such as sequencing adapters, primers, sample-, cell-, and molecule-specific barcodes. Despite a fairly high level of current knowledge, during the protocol development process researches often have to deal with various kinds of unexpected experiment outcomes, which result either from lack of information, lack of knowledge, or defects in reagent manufacturing. Detection and analysis of service sequences, their distribution and linkage may provide important information for protocol optimization. Here we introduce FastContext, a tool designed to analyze NGS read structure, based on sequence features found in reads, and their relative position in the read. The algorithm is able to create human readable read structures with user-specified patterns, to calculate counts and percentage of every read structure. Despite the simplicity of the algorithm, FastContext may be useful in read structure analysis and, as a result, can help better understand molecular processes that take place at different stages of NGS library preparation. The project is open-source software, distributed under GNU GPL v3, entirely written in the programming language Python, and based on well-maintained packages and commonly used data formats. Thus, it is cross-platform, may be patched or upgraded by the user if necessary. The FastContext package is available at the Python Package Index (https://pypi.org/project/FastContext), the source code is available at GitHub (https://github.com/regnveig/FastContext).

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data.

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

Elements of the methodology of teaching the theme "Raster computer graphics" with the help of the GIMP free graphic editor at the level of basic general education

The article describes the framework of the methodology for teaching basic school students the technology of both processing digital photography and creating raster graphic images from scratch. As a software tool, it is proposed to use the free GIMP raster editor. Raster computer graphics is more complex in relation to vector graphics, since a large number of tools are used in raster graphics, and brush graphic tools (Brush, Pencil, Airbrush) are also actively used, the effective use of which requires user of basic artistic skills. The choice of the free graphic editor GIMP is due to a number of significant advantages of this software tool over analogues. These include the GNU GPL license for its use, cross-platform, relatively low system requirements, functionality close to professional raster editors, as well as a significant stack of educational and methodological literature on the technology of using GIMP in the educational process, that is generally not typical for free software. The complex user interface of the GIMP editor is due to the large number of tools and the full functionality of this software and can be mastered by basic school students, provided that an effective methodology for teaching raster graphics is used in the educational process. When creating a framework for teaching schoolchildren about raster graphics, the technology for using all groups of GIMP editor tools is demonstrated in the process of conducting practical work aimed at creating various raster graphics, the complexity of which gradually increases in proportion to the number of tools mastered by students. Also, the proposed teaching methodology takes into account all the main structural elements of professional raster graphics, namely: the technology for the effective use of the group of operations for selecting image fragments, the technology for the effective use of the group for the transformation operation, the technology for the effective use of the group of drawing tools, image layers, color channels, contours, built-in filters, creation of text elements, creation of GIF-animation.

IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics.

In proteomics, data-independent acquisition (DIA) has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA.

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.

Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.