Python Interfaces for the Smoldyn Simulator

Low Level ◽

Link Type ◽

Programming Interface ◽

AbstractMotivationSmoldyn is a particle-based biochemical simulator that is frequently used for systems biology and biophysics research. Previously, users could only define models using text-based input or a C/C++ applicaton programming interface (API), which were convenient, but limited extensibility.ResultsWe added a Python API to Smoldyn to improve integration with other software tools such as Jupyter notebooks, other Python code libraries, and other simulators. It includes low-level functions that closely mimic the existing C/C++ API and higher-level functions that are more convenient to use. These latter functions follow modern object-oriented Python conventions.AvailabilitySmoldyn is open source and free, available athttp://www.smoldyn.org, and can be installed with the Python package managerpip. It runs on Mac, Windows, and [email protected] informationDocumentation is available athttp://www.smoldyn.organdhttps://smoldyn.readthedocs.io.

ccNetViz: a WebGL-based JavaScript library for visualization of large networks

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4527-4529

Author(s):

Ales Saska ◽

David Tichy ◽

Robert Moore ◽

Achilles Rasquinha ◽

Caner Akdas ◽

...

Keyword(s):

Systems Biology ◽

Complex Networks ◽

Open Source ◽

High Speed ◽

A Priori ◽

Network Visualization ◽

Supplementary Data ◽

Web Based ◽

Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

PyRice: a Python package for querying Oryza Sativa databases

10.1101/2020.04.20.049742 ◽

2020 ◽

Author(s):

Quan Do ◽

Ho Bich Hai ◽

Pierre Larmande

Keyword(s):

Oryza Sativa ◽

Heterogeneous Data ◽

Web Based ◽

Domain Experts ◽

Link Type ◽

Heterogeneous Data Sources ◽

Query System ◽

Gene Information ◽

AbstractSummaryCurrently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programming API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.Availability and implementationhttps://github.com/SouthGreenPlatform/PyRiceDocumentationhttps://[email protected] informationMITSupplementary informationSupplementary data are available online.

AGEpy: a Python package for computational biology

10.1101/450890 ◽

2018 ◽

Cited By ~ 1

Author(s):

Franziska Metge ◽

Robert Sehlke ◽

Jorge Boucas

Keyword(s):

Computational Biology ◽

Open Source ◽

High Throughput ◽

Biological Data ◽

Command Line ◽

High Throughput Analysis ◽

Throughput Analysis ◽

Link Type ◽

Biological Meaning ◽

AbstractSummary:AGEpy is a Python package focused on the transformation of interpretable data into biological meaning. It is designed to support high-throughput analysis of pre-processed biological data using either local Python based processing or Python based API calls to local or remote servers. In this application note we describe its different Python modules as well as its command line accessible toolsaDiff,abed,blasto,david, andobo2tsv.Availability:The open source AGEpy Python package is freely available at:https://github.com/mpg-age-bioinformatics/AGEpy.Contact:[email protected]

DNA Chisel, a versatile sequence optimizer

10.1101/2019.12.16.877480 ◽

2019 ◽

Author(s):

Valentin Zulkower ◽

Susan Rosser

Keyword(s):

Open Source ◽

Dna Sequence ◽

Web Application ◽

Optimization Problems ◽

Sequence Optimization ◽

Sequence Design ◽

Optimization Framework ◽

Link Type ◽

Dna Sequence Design

AbstractMotivationAccounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.ResultsDNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations.Availabilityas a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (code and documentation at https://github.com/Edinburgh-Genome-Foundry/DNAChisel)[email protected] informationattached.

dms2dfe: Comprehensive Workflow for Analysis of Deep Mutational Scanning Data

10.1101/072645 ◽

2016 ◽

Cited By ~ 2

Author(s):

Rohan Dandage ◽

Kausik Chakraborty

Keyword(s):

Noise Reduction ◽

High Throughput ◽

Critical Issue ◽

Supplementary Data ◽

Selection Pressures ◽

Link Type ◽

Supplementary Material ◽

End To End ◽

SummaryHigh throughput genotype to phenotype (G2P) data is increasingly being generated by widely applicable Deep Mutational Scanning (DMS) method. dms2dfe is a comprehensive end-to-end workflow that addresses critical issue with noise reduction and offers variety of crucial downstream analyses. Noise reduction is carried out by normalizing counts of mutants by depth of sequencing and subsequent dispersion shrinkage at the level of calculation of preferential enrichments. In downstream analyses, dms2dfe workflow provides identification of relative selection pressures, potential molecular constraints and generation of data-rich visualizations.Availabilitydms2dfe is implemented as a python package and it is available at https://kc-lab.github.io/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

EpyNN: Educational python for Neural Networks

10.1101/2021.12.06.470764 ◽

2021 ◽

Author(s):

Florian Malard ◽

Laura Danner ◽

Emilie Rouzies ◽

Jesse G Meyer ◽

Ewen Lescop ◽

...

Keyword(s):

Neural Networks ◽

Graphical Representation ◽

Source Code ◽

Application Programming Interface ◽

Practical Implementation ◽

Data Preparation ◽

Link Type ◽

Application Programming ◽

Programming Interface

AbstractSummaryArtificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch.AvailabilityEpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn.ContactStéphanie Olivier-Van-Stichelen, [email protected] InformationSupplementary files and listings.

BiomeHorizon: visualizing microbiome time series data in R

10.1101/2021.08.29.458140 ◽

2021 ◽

Author(s):

Isaac Fink ◽

Richard J. Abdill ◽

Ran Blekhman ◽

Laura Grieneisen

Keyword(s):

Time Series ◽

Open Source ◽

Time Series Data ◽

R Package ◽

Series Data ◽

Link Type ◽

Microbiome Research ◽

Microbiome Data ◽

Over Time

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

Proxi: a Python package for proximity network inference from metagenomic data

10.1101/357764 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yasser EL-Manzalawy

Keyword(s):

Network Inference ◽

Nearest Neighbor ◽

Microbial Interactions ◽

Metagenomic Data ◽

Metagenomic Sequencing ◽

Proximity Graphs ◽

Link Type ◽

Technological Advances ◽

AbstractSummary: Recent technological advances in high-throughput metagenomic sequencing have provided unique opportunities for studying the diversity and dynamics of microbial communities under different health or environmental conditions. Graph-based representation of metagenomic data is a promising direction not only for analyzing microbial interactions but also for a broad range of machine learning tasks including feature selection, classification, clustering, anomaly detection, and dimensionality reduction. We present Proxi, an open source Python package for learning different types of proximity graphs from metagenomic data. Currently, three types of proximity graphs are supported: k-nearest neighbor (k-NN) graphs; radius-nearest neighbor (r-NN) graphs; and perturbed k-nearest neighbor (pk-NN) graphs.Availability: Proxi Python source code is freely available at https://bitbucket.org/idsrlab/proxi/.Contact:[email protected] information: Tutorials and online documentation are available at https://proxi.readthedocs.io

CellTool: an open source software combining bio-image analysis and mathematical modeling

10.1101/454256 ◽

2018 ◽

Author(s):

Georgi Danovski ◽

Teodora Dyankova ◽

Stoyno Stoynov

Keyword(s):

Mathematical Modeling ◽

Image Analysis ◽

Open Source ◽

Open Source Software ◽

Time Lapse ◽

Data Sets ◽

Link Type ◽

Sample Data ◽

Supplementary Material

AbstractSummaryWe present CellTool, a stand-alone open source software with a Graphical User Interface for image analysis, optimized for measurement of time-lapse microscopy images. It combines data management, image processing, mathematical modeling and graphical presentation of data in a single package. Multiple image filters, segmentation and particle tracking algorithms, combined with direct visualization of the obtained results make CellTool an ideal application for rapid execution of complex tasks. In addition, the software allows for the fitting of the obtained results to predefined or custom mathematical models. Importantly, CellTool provides a platform for easy implementation of custom image analysis packages written on a variety of programing languages.Availability and ImplementationCellTool is a free software available for MS Windows OS under the terms of the GNU General Public License. Executables and source files, supplementary information and sample data sets are freely available for download at URL: https://dnarepair.bas.bg/software/CellTool/[email protected]; [email protected];Supplementary informationSupplementary data are available at URL: https://dnarepair.bas.bg/software/CellTool/Program/CellTool_UserGuide.pdf

pyrpipe: a python package for RNA-Seq workflows

10.1101/2020.03.04.925818 ◽

2020 ◽

Author(s):

Urminder Singh ◽

Jing Li ◽

Arun Seetharam ◽

Eve Syrkin Wurtele

Keyword(s):

Detailed Analysis ◽

Source Code ◽

Workflow Management ◽

Object Oriented ◽

Third Party ◽

Rna Seq ◽

Link Type ◽

Computing Environments ◽

High Level ◽

Implementing RNA-Seq analysis pipelines is challenging as data gets bigger and more complex. With the availability of terabytes of RNA-Seq data and continuous development of analysis tools, there is a pressing requirement for frameworks that allow for fast and efficient development, modification, sharing and reuse of workflows. Scripting is often used, but it has many challenges and drawbacks. We have developed a python package, python RNA-Seq Pipeliner (pyrpipe) that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in python, in an object-oriented manner. pyrpipe provides high level APIs to popular RNA-Seq tools. Pipelines can be customized by integrating new python code, third-party programs, or python libraries. Researchers can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments. 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. All source code is available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source or from PyPi (https://pypi.org/project/pyrpipe). Documentation is available on Read the Docs (http://pyrpipe.rtfd.io).