Topoly: Python package to analyze topology of polymers.
Ontology highlight
ABSTRACT:
SUBMITTER: Dabrowski-Tumanski P
PROVIDER: S-EPMC8138882 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus, which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment. To address this challenge, constraint-based reconstruction of biochemically accurate genome-scale metabolic models (GEMs) networks of microorganisms is mandatory. Our workflow applies constraint-based modeling (CBM), simulates the metabolism between species in a given microbiome, and facilitates generating novel hypotheses on microbial interactions. Utilizing this workflow, we hope to gain a better understanding of interactions from the metabolic modeling perspective. This article presents nasal community modeling workflow (NCMW)-a python package based on GEMs of species as a starting point for understanding the composition of the nasal microbiome community. The package is constructed as a step-by-step mathematical framework for metabolic modeling and analysis of the nasal microbial community. Using constraint-based models reduces the need for culturing species in vitro, a process that is not convenient in the environment of human noses. Availability: NCMW is freely available on the Python Package Index (PIP) via pip install NCMW. The source code, documentation, and usage examples (Jupyter Notebook and example files) are available at https://github.com/manuelgloeckler/ncmw.
| S-EPMC9580955 | biostudies-literature
Project description:UNLABELLED:DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. AVAILABILITY: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.
| S-EPMC4837986 | biostudies-literature
Project description:BackgroundEven though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.ResultsWe present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.ConclusionCosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
| S-EPMC7597035 | biostudies-literature
Project description:The availability of terabytes of RNA-Seq data and continuous emergence of new analysis tools, enable unprecedented biological insight. There is a pressing requirement for a framework that allows for fast, efficient, manageable, and reproducible RNA-Seq analysis. We have developed a Python package, (pyrpipe), that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in Python, in an object-oriented manner. pyrpipe provides access to popular RNA-Seq tools, within Python, via high-level APIs. Pipelines can be customized by integrating new Python code, third-party programs, or Python libraries. Users can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments, and enabling efficient resource management. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in Python and is compatible with Python versions 3.6 and higher. To illustrate the rich functionality of pyrpipe, we provide case studies using RNA-Seq data from GTEx, SARS-CoV-2-infected human cells, and Zea mays. All source code is freely available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source, from PyPI (https://pypi.org/project/pyrpipe), or from bioconda (https://anaconda.org/bioconda/pyrpipe). Documentation is available at (http://pyrpipe.rtfd.io).
| S-EPMC8168212 | biostudies-literature
Project description:MotivationDNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data.ResultsWe developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) 'CpG position modules' focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) 'CpG signal modules' are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) 'differential CpG analysis modules' focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation.Availability and implementationCpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8275978 | biostudies-literature
Project description:We present dingo, a Python package that supports a variety of methods to sample from the flux space of metabolic models, based on state-of-the-art random walks and rounding methods. For uniform sampling, dingo's sampling methods provide significant speed-ups and outperform existing software. Indicatively, dingo can sample from the flux space of the largest metabolic model up to now (Recon3D) in less than a day using a personal computer, under several statistical guarantees; this computation is out of reach for other similar software. In addition, dingo supports common analysis methods, such as flux balance analysis and flux variability analysis, and visualization components. dingo contributes to the arsenal of tools in metabolic modelling by enabling flux sampling in high dimensions (in the order of thousands).Availability and implementationThe dingo Python library is available in GitHub at https://github.com/GeomScale/dingo and the data underlying this article are available in https://doi.org/10.5281/zenodo.10423335.
| S-EPMC10997433 | biostudies-literature
Project description:pyGeno is a Python package mainly intended for precision medicine applications that revolve around genomics and proteomics. It integrates reference sequences and annotations from Ensembl, genomic polymorphisms from the dbSNP database and data from next-gen sequencing into an easy to use, memory-efficient and fast framework, therefore allowing the user to easily explore subject-specific genomes and proteomes. Compared to a standalone program, pyGeno gives the user access to the complete expressivity of Python, a general programming language. Its range of application therefore encompasses both short scripts and large scale genome-wide studies.
| S-EPMC5022704 | biostudies-literature
Project description:Theoretical/computational description of excited state molecular dynamics is nowadays a crucial tool for understanding light-matter interactions in many materials. Here we present an open-source Python-based nonadiabatic molecular dynamics program package, namely PyUNIxMD, to deal with mixed quantum-classical dynamics for correlated electron-nuclear propagation. The PyUNIxMD provides many interfaces for quantum chemical calculation methods with commercial and noncommercial ab initio and semiempirical quantum chemistry programs. In addition, the PyUNIxMD offers many nonadiabatic molecular dynamics algorithms such as fewest-switch surface hopping and its derivatives as well as decoherence-induced surface hopping based on the exact factorization (DISH-XF) and coupled-trajectory mixed quantum-classical dynamics (CTMQC) for general purposes. Detailed structures and flows of PyUNIxMD are explained for the further implementations by developers. We perform a nonadiabatic molecular dynamics simulation for a molecular motor system as a simple demonstration.
| S-EPMC8362049 | biostudies-literature
Project description:SummaryGenerate Indexes for Libraries (GIL) is a software tool for generating primers to be used in the production of multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs.Availability and implementationGIL is written in Python and is freely available on GitHub under the MIT license: https://github.com/de-Boer-Lab/GIL and can be accessed as a web-application implemented in Streamlit at https://dbl-gil.streamlitapp.com.
| S-EPMC10246578 | biostudies-literature
Project description:The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this article, we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python and can be used interactively or through the command line using pre-packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).
| S-EPMC6454532 | biostudies-literature