University of Warwick

There are 3 options when it comes to using our applications:

1. Via the CyVerse Discovery Environment. This is the recommended approach to a new user. This is the easiest option since a full user interface is provided to the user.
2. Using the Docker images that are available on our Docker Hub repository 🐳. Each application/tool has a corresponding image.
3. With the source codes that are hosted on our Github repository . This approach will give you more information of how the application actually works. We are always looking to improve our code, so feel free to send us a pull request.

We give details on the first option and interested users may try the other two options with reference to the documentations we have for each application.

CyVerse Discovery Environment

All of our applications on the CyVerse Discovery Environment are searchable using the "uk cyverse" keyword in the application search box.

Below is a screenshot showing the search in action:

A full list of applications developed by the Warwick team is given in the table:

Application	Description	Run on CyVerse *
	Sequence Alignment
APPLES	A set of tools to analyse promoter sequences on a genome-wide scale	DE
	Footprint Identification
Wellington Bootstrap	An algorithm for the identification of regions occupied by proteins in DNase-seq data, performing a differential analysis between two samples	DE
Wellington Footprint	An algorithm for the identification of regions occupied by proteins in DNase-seq data	DE
	Differencial Expression
GP2S	A differential expression algorithm for time series data with a two condition (eg. control/treated) experimental design	DE
Gradient Tool	An algorithm for the identification of the time of change from single condition time course expression data	DE
	Network Inference
CSI	A network inference algorithm capable of inferring causal regulatory network models from time course expression data	DE
hCSI	An expansion of CSI network inference to handle multiple time course datasets	DE
oCSI	An expansion of CSI network inference to handle data from multiple organisms	DE
	Clustering / Biclustering
BHC	A clustering algorithm for expression data originally made available in R, allows for the analysis of both time course or multiple static datasets	DE
TCAP	A clustering algorithm for time course expression data, identifies complex regulatory groups thanks to a rich information measure	DE
Wigwams	An algorithm for the extraction of gene groups co-regulated across subsets of multiple time course datasets	DE
	Transcription Factor Motif Enrichment
HMT	A transcription factor binding site overrepresentation analysis algorithm for known motifs	DE
MEME-LaB	A transcription factor binding site overrepresentation analysis algorithm with novel motif discovery	DE

* - A CyVerse account is required. Register here if necessary.

These applications are also accessible through our local Discovery Environment