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5 materials found

Resource type: Jupyter notebook  or Computer Software 

BioExcel Building Blocks (BioBB) Training Material

BioExcel Building Blocks (BioBB) is a software library for interoperable biomolecular simulation workflows, built within the BioExcel CoE project, and developed following best practices on software development aligned with ELIXIR.

Keywords: molecular dynamics, Docking, Modeling, life sciences

Resource type: Jupyter notebook

DE-Sim examples, tutorials, and documentation

DE-Sim is an open-source, Python-based object-oriented discrete-event simulation (DES) tool that makes it easy to use large, heterogeneous datasets and high-level data science tools such as NumPy, Scipy,...

Scientific topics: Computational biology, Mathematics, Computer science, Simulation experiment

Operations: Visualisation, Modelling and simulation

Keywords: data-driven modeling, Computational modelling, discrete-event simulation, DES, object-oriented programming, Python, data visualization, Data Science

Resource type: examples, Tutorial, Jupyter notebook, API reference

How to use V-pipe (SARS-CoV-2 data)

This tutorial demonstrate how to use V-pipe - Bioinformatics pipeline for the analysis of next-generation sequencing data derived from intra-host viral populations.

For the purpose of this toturial, we use the sars-cov2 branch of V-pipe and analyse SARS-CoV-2 runs downloaded from NCBI SRA.

Scientific topics: Workflows, Genetic variation, Virology

Operations: Read pre-processing, Alignment, Variant calling

Keywords: coronavirus, NGS bioinformatics, Variant-calling

Resource type: Computer Software, workflow

ELIXIR Beacons (at ISMB / ECCB)

Large cohorts, with potentially millions of participants, are needed to understand the genetic and molecular signatures of diseases, and they provide a cornerstone for the creation of personalised treatments. The ELIXIR::GA4GH Strategic Partnership will facilitate the responsible sharing of these...

Keywords: beacon

Resource type: Slides, Computer Software

Introductory image processing on biological images using python.

This is an image processing practical for students with an interest in analysing biologically derived images. It is written in the language python and utilises jupyter notebook for annotation and visualisation of the code. The practical is beginner level, although some knowledge of python is...

Resource type: Jupyter notebook, PDF