Release notes

Written By Marko Marinkovic

PyImageJ: Python wrapper for ImageJ2 project is public on CGC

The PyImageJ: Python wrapper for ImageJ2 project that serves as a comprehensive tutorial for users interested in leveraging PyImageJ for image analysis and processing is now publicly available on CGC. It features one Data Studio interactive analysis, written in Python, with step-by-step demonstrations and examples showcasing the integrative capabilities of PyImageJ.

PyImageJ provides a set of wrapper functions for integration between ImageJ2 and Python. ImageJ is an open-source image processing program designed for scientific multidimensional images. PyImageJ allows you to leverage the power of ImageJ's image processing capabilities directly from within Python, combining the strengths of both environments. A major advantage of this approach is the ability to combine ImageJ and ImageJ2 with other tools available from the Python software ecosystem, including NumPy, SciPy, scikit-image, and many more.

 

UmetaFlow: Untargeted Metabolomics Workflow for Data Processing and Analysis published

We've just published the UmetaFlow: Untargeted Metabolomics Workflow for Data Processing and Analysis Public Project. This project was developed in collaboration with the OpenMS team and serves as a comprehensive tutorial for users interested in metabolomics data analysis. It includes a Data Studio interactive analysis, written in Python, with step-by-step demonstrations and examples highlighting UmetaFlow's capabilities.
UmetaFlow utilizes the pyOpenMS package, a Python wrapper for the OpenMS algorithms, allowing integration with other commonly used Python data science modules. This allows for interactive computing, easy data exploration and visualization, and rapid prototyping of new analytical steps.

The project includes four Jupyter notebooks, each focusing on different aspects of the untargeted metabolomics analysis workflow:

  • Preprocessing: A crucial step in metabolomics data mining for transforming the raw data to a table of metabolic features. This part of the workflow uses various OpenMS algorithms covering data cleaning and preparation, including loading mzML files, performing precursor correction, mass trace detection, elution peak detection, feature finding, and feature grouping into a consensus map.

  • Requantification: Addresses missing values by re-detecting and extracting features with missing intensities across samples, ensuring comparability and consistency of the data.

  • GNPSExport: Facilitates the export of processed data to the Global Natural Products Social (GNPS) molecular networking platform for further analysis and visualization.

  • SiriusExport: Enables the export of data to the SIRIUS software for compound identification and structural elucidation based on fragmentation spectra.

Variant Annotation Apps Public Project published

Exciting news about an update to one of our public projects. We recently published the Variant Annotation Apps public project which is meant to replace the Variant Browser. This project is a starting point for users to annotate their VCF files and contains public files and apps to get started.  

FusionCatcher 1.33 now available

FusionCatcher tool has been published on the CGC. FusionCatcher identifies somatic novel or known fusion genes, translocations, and chimeras within RNA-seq data.

exceRpt WF published

The exceRpt pipeline has been published on the CGC. The WF performs preprocessing and identification of smallRNAs, and results visualisation. 

Cell Ranger tools published

Cell Ranger Aggr, Cell Ranger Reanalyze, Cell Ranger VDJ and Cell Ranger Count tools from Cell Ranger toolkit v8.0.1 are published on the CGC. 

Cell Ranger is a set of analysis pipelines that perform sample demultiplexing, barcode processing, single cell 3' and 5' gene counting, V(D)J transcript sequence assembly and annotation, and Feature Barcode analysis from single cell data.

 

Severus and GeneFuse published

Severus 1.1 and GeneFuse 0.8.0 have been published on the CGC. 

Severus 1.1 calls somatic structural variants in long reads data, while GeneFuse detects and visualizes known gene fusions from FASTQ files.

 

JuLI and FuSeq_WES apps published

JuLI (0.1.6.2) and FuSeq_WES (1.0.0, 4c55ebb) have been published on the CGC. 

JuLI and FuSeq_WES are callers for gene fusions in targeted/WES data. JuLI controlpanel and FuSeq_WES Prepare References utility tools have also been published.

Marko Marinkovic
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Release notes