Workflow flavors
In this directory you can find the alternative pre-configurations of GraphClust-2 as flavors tailored for different use-case scenarios.
- Preconfigured flavors of the workflow
- The MotifFinder workflow flavor targets identifying a handful of local signals/motifs under the likely presence of noise and sequence context.
- The pre-configured main workflows perform best for clustering and partitioning a set of RNA sequences with quasi defined structure boundary signals (e.g. ncRNAs or data from genomic screenings with tools such as CMfinder or RNAz screens). Usually up to 3 rounds of clustering, depending on the size of input and classes, would be enough to identify the homologs.
- For large datasets with thousands of sequences, further iterations of clustering can be helpful. The sub-workflow based flavors are recommended for such cases available under extra-workflows/with-subworkflow/
- Auxiliary workflows
- The auxiliary workflows provide alternative ways to cluster genomic data beyond the classic FASTA input.
Configuring the workflows
Please proceed with the interactive tour named GraphClust workflow step by step, available under Help->Interactive Tours and also check the references.
An intuitive tutorial highlighting the use-case scenarios and the few parameters that can be adapted according to the scenarios will be provided soon here.