Göteborgs universitet

Tool evaluation projects

It is common that within a specific analysis, several tools are available and, sometimes the lack of golden standards makes it difficult to select the best tool if it has not been previously evaluated. These projects show some of our experiences while evaluating these tools.

Mutational signatures

BCF contributor(s): Katarina Truvé. In collaboration with Anna Rohlin (Laboratory Medicine at UGoT)

This project compares two tools: DeconstructSigs and SigPprofiler, to identify most suitable tool in the identification of mutational signatures in cancer. It could be proposed that signatures that overlap between the tools would be more likely to be correct, but more analysis are needed to understand how to best extract accurate information and how to interpret the results.

Pathway analysis software

BCF contributor(s): Annelie Angerfors

We compared licensed software (QIAGEN IPA) versus open source tools (DAVID, Enrichr, Reactome and Cytoscape) to identify the best tool focusing on how easy is to understand the overall application, the presentation of the results, the navigation of the site, scalability and automation. After the evaluation, we decided to use Reactome, an open source database that can be accessed via their website, or the Cytoscape platform or R, using the ReactomePA R packages.

Pseudotime analysis tools for snRNAseq data

BCF contributor(s): Vanja Börjesson. In collaboration with Malin Johansson (UGoT)

We have benchmarked five tools for pseudotime analysis: Monocle2, Monocle3, Slicer, Destiny and Scanpy. We compared their time efficiency and memory usage, their ability to control filtering, normalization, the layout of the output figures and the installation  process. All five tools predict the same trajectory of our test dataset. All tools were easy to install and use. Slicer was by far the most time consuming tool. Monocle and Scanpy implement different algorithms sfor trajectory development, filtering and normalization fo the data, while having many options for visualizing differentially expressed genes.