Introduction In this post I will present example SPARQL queries against the Pseudomonas fluorescens SBW25 knowledge graph (SBW25KG). The knowlegde graph was derived from the manually created annotation in gff3 format, as explained in a previous post. The queries are run against a local instance of the apache-jena-fuseki triplestore.
Introduction In this article, I will describe the steps taken to generate a RDF (Resource Description Format) datastructure starting from a gff3 formatted genome annotation file. The annotation file in question is the new reference annotation for Pseudomonas fluorescens strain SBW25. Required packages I will make use of the following python packages: gffutils to read the gff3 file into a sqlite database.
Getting your data into the database is one of the most frequent tasks when working with OMERO.
As every year, some PostDocs and Staff Scientists offer courses on computing, programming, data analysis and visualization and related topics. Requirements : Some courses require that participants already have certain levels of experience and knowledge. Before signing up, please assess for yourself if you feel comfortable with these requirements. If in doubt, please contact the course responsible.
On May 5 & 6 2021, I took part in the Workshop “Kompetenz Forschungsdatenmanagement” organized by the Max Planck Digital Library. Day 2 featured a full session on “Reproducible Science with Jupyter” with a presentation by Hans Fangohr (slides available here) followed by an interactive hands-on tutorial. In part 1 of the tutorial, I step through a data analysis workflow based on the Johns-Hopkins University CoViD19 dataset from github.
Parallel python with dask and jupyter The dask framework provides an incredibly useful environment for parallel execution of python code in interactive settings (e.g. jupyter) or batch mode. Its key features are (from what I’ve seen so far): Representation of threading, multiprocessing, and distributed computing with one unified API and CLI.
On Dec. 10 & 11, Nikoletta Glynatsi and I ran our first Workshop on “Research Software Development”. Far from appraising myself, but judging from the feedback, it was a big success. For two days, we taught best practises in writing software (exemplified with python), using git for version control, collaborating on gitlab projects and employing gitlab’s built-in continuous integration tools to run automated tests and build a reference manual.
Running MATLAB scripts on HPC Today, the question came up how to run MATLAB code on HPC featuring a SLURM scheduler. The syntax for running matlab on the command line is indeed a bit counterintuitive, at least if you are (like me) used to running python or R scripts.
This one comes a bit late but for the sake of completeness: Our software paper on 2D stochastic cancer simulations with CancerSim is out: doi:10.21105/joss.02436.
Inserting images in a jupyter notebook is just drag and drop: This will automagically produce the image link at the drop position. And after executing the cell, the image is rendered So far so good. But ever tried to convert a notebook with embedded images to pdf or html (slides)? My first guess was: Menu -> File -> Export Notebook As -> PDF.
Our (Nikoleta Glynatsi and myself) Research Software Development workshop starts tomorrow. All code and presentations are available here: https://gitlab.gwdg.de/glynatsi/rsd-workshop.