Welcome to GraphClust2 Galaxy server for clustering of RNAs according to sequence and secondary structures similarities.

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.

Content

• Getting started
  • Results from the paper, shared histories
• GraphClust pipeline overview
• Support & Bug Reports
• References

Getting started

Interactive tours

GraphClust2 rapid start

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 workflow (direct link to the basic tour). Please use your personal user-password for logging in. You can check the other tours for a more general introduction to the Galaxy interface.

Galaxy interface

Are you new to Galaxy, or returning after a long time, and looking for help to get started? Take a guided tour through Galaxy’s user interface.

GraphClust2 repository

Please also refer to the GraphClust2 repository for other deployment options and manuals.

Video tutorial

This video tutorial can be helpful to get a visually comprehensive introduction on setting-up and running GraphClust2. The video starts with setting up the docker Galaxy server that can be skipped through using this server.

Workflow flavors

A comprehensive set of pre-configured flavors of GraphClust2 are provided and described inside the workflows directory There you can find the alternative pre-configurations of GraphClust-2 as flavors tailored for different use-case scenarios.

Workflows flavors on this server

Below workflows can be directly accessed on the public server. For the extended description and alternatives please refer to the github workflows directory

• The MotifFinder workflow flavor targets identifying a handful of local signals/motifs under the likely presence of noise and sequence context.

  • MotifFinder: GraphClust-MotifFinder

• 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 one to three rounds of clustering would be enough for typical scenarios. You may find further suitable pre-configured flavors from the github directory page.

  • Workflow main: GraphClust_1r
  • Workflow main, pre-configured for two rounds : GraphClust_2r

Import or upload a workflow

To import or upload additional workflow flavors (e.g. from 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

Results from the paper, shared histories

The histories shared and linked below, corresponds to the clustering analysis and evaluations that are performed and presented in the GraphClust2 paper.

lncRNA structure conservation analysis

• NEAT1 clustering history
• MALAT1 clustering history
• HOTAIR clustering history
• XIST clustering history
• FTL clustering history

Orthologous genomic sequence extraction of lncRNAs

• MAF to fasta conversions

CLIP motif finder

• SLBP clustering history
• SLBP RF00032 seed (mixed with 98.5% background) clustering history
• Roquin1

Scalability demonstration

• Marine metatranscriptome clustering history

GraphClust pipeline overview

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:

Input

The input to the workflow is a set of putative RNA sequences in FASTA format. Inside the data directory within the repository, you can find examples of the input format.

Output

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.

Configuring the workflows:

Please proceed with the interactive tour named GraphClust workflow step by step, available under Help->Interactive Tours Please refer to the in-wrapper help descriptions the tools documentations and the repository’s FAQs for checking the important parameters.

Support & Bug Reports

You can file an github issue or find our contact information in the Backofen lab page.

References

• Miladi, Milad, Eteri Sokhoyan, Torsten Houwaart, Steffen Heyne, Fabrizio Costa, Bjoern Gruening, and Rolf Backofen. “GraphClust2: 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