GraphClust2 is a workflow for scalable clustering of RNAs based on sequence and secondary structures feature. GraphClust2 is implemented within the Galaxy framework and consists a set of integrated Galaxy tools and flavors of the linear-time clustering workflow.
Table of Contents
- Table of Contents
- Usage - How to run GraphClust2
- Workflow overview
- Support & Bug Reports
GraphClust2 on European Galaxy Server
GraphClust2 is accessible on European Galaxy server at:
It is also possible to run GraphClust2 as a stand-alone solution using a Docker container that is a pre-configured flavor of the official Galaxy Docker image. This Docker image is a flavor of the Galaxy Docker image customized for GraphClust2 tools, tutorial interactive tours and workflows.
Installation and Setup
To run GraphClust2 locally Docker client is required. Docker supports the three major desktop operating systems Linux, Windows and Mac OSX. Please refer to thw Docker installation guideline for details.
A GUI client can also be used under Windows and Mac OS. Please follow the graphical instructions for using Kitematic client here.
- Minimum 8GB memory
- Minimum 20GB free disk storage space, 100GB recommended.
Supported operating systems
GraphClust2 has been tested on these operating systems:
- Windows : 10 using Kitematic
- MacOSx: 10.1x or higher using Kitematic
- Linux: Kernel 4.2 or higher, preferably with aufs support (see FAQ)
Running the docker instance
From the command line:
docker run -i -t -p 8080:80 backofenlab/docker-galaxy-graphclust
For details about the docker commands please check the official guide here. Galaxy specific run options and configuration supports for computation grid systems are detailed in the Galaxy Docker repository.
Using graphic interface (Windows/MacOS)
Please check this step-by-step guide.
Installation on a Galaxy instance
A running demo instance of GraphClust2 is available at http://220.127.116.11:8080/. Please note that this instance is simply a Cloud instance of the provided Docker container, intended for rapid inspections and demonstration purposes. The computation capacity is limited and currently it is not planned to have a long-time availability. We recommend to follow instructions above. Please contact us if you prefer to keep this service available.
Usage - How to run GraphClust2
Browser access to the server
Please register on our European Galaxy server https://usegalaxy.eu and use your authentication information to access the customized sub-domain [https://graphclust.usegalaxy.eu]. Guides and tutorial are available in the server welcome home page.
After running the Galaxy docker, a web server is established under the host IP/URL and designated port (default 8080).
- Inside your browser goto IP/URL:PORT
- Following same settings as previous step
You might find this Youtube tutorial helpful to get a visually comprehensive introduction on setting-up and running GraphClust2.
Interactive Tours are available for Galaxy and GraphClust2. To run the tours please on top panel go to Help→Interactive Tours and click on one of the tours prefixed GraphClust. You can check the other tours for a more general introduction to the Galaxy interface.
Import additional workflows
To import or upload additional workflow flavors (e.g. from extra-workflows directory), on the top panel go to Workflow menu. On top right side of the screen click on "Upload or import workflow" button. You can either upload workflow from your local system or by providing the URL of the workflow. Log in is necessary to access into the workflow menu. The docker galaxy instance has a pre-configured easy! info that can be found by following the interactive tour. You can download workflows from the following links
The pre-configured flavors of GraphClust2 are provided and described inside the workflows directory
Workflows on the running server
Below workflows can be directly accessed on the public server:
- MotifFinder: GraphClust-MotifFinder
- Workflow main: GraphClust_1r
- Workflow main, preconfigured for two rounds : GraphClust_2r
The pipeline for clustering RNA sequences and structured motif discovery is a multi-step pipeline. Overall it consists of three major phases: a) sequence based pre-clustering b) encoding predicted RNA structures as graph features c) iterative fast candidate clustering then refinement
Below is a coarse-grained correspondence list of GraphClust2 tool names with each step:
|Stage||Galaxy Tool Name||Description|
|1||Preprocessing||Input preprocessing (fragmentation)|
|2||fasta_to_gspan||Generation of structures via RNAshapes and conversion into graphs|
|3||NSPDK_sparseVect||Generation of graph features via NSPDK|
|4||NSPDK_candidateClusters||min-hash based clustering of all feature vectors, output top dense candidate clusters|
|5||PGMA_locarna,locarna, CMfinder||Locarna based clustering of each candidate cluster, all-vs-all pairwise alignments, create multiple alignments along guide tree, select best subtree, and refine alignment.|
|6||Build covariance models||create candidate model|
|7||Search covariance models||Scan full input sequences with Infernal's cmsearch to find missing cluster members|
|8,9||Report results and conservation evaluations||Collect final clusters and create example alignments of top cluster members|
The input to the workflow is a set of putative RNA sequences in FASTA format. Inside the
data directory you can find examples of the input format. The labeled datasets are based on Rfam annotation that are labeled with the associated RNA family.
The output contains the predicted clusters, where similar putative input RNA sequences form a cluster. Additionally overall status of the clusters and the matching of cluster elements is reported for each cluster.
Support & Bug Reports
The manuscript is currently under prepration/revision. If you find this resource useful, please cite the zenodo DOI of the repo or contact us.
- Miladi, Milad, Eteri Sokhoyan, Torsten Houwaart, Steffen Heyne, Fabrizio Costa, Bjoern Gruening, and Rolf Backofen. "Empowering the annotation and discovery of structured RNAs with scalable and accessible integrative clustering." bioRxiv (2019): 550335. doi: https://doi.org/10.1101/550335
- Milad Miladi, Björn Grüning, & Eteri Sokhoyan. BackofenLab/GraphClust-2: Zenodo. http://doi.org/10.5281/zenodo.1135094
- GraphClust-1 methodology (S. Heyne, F. Costa, D. Rose, R. Backofen; GraphClust: alignment-free structural clustering of local RNA secondary structures; Bioinformatics, 2012) available at http://www.bioinf.uni-freiburg.de/Software/GraphClust/