Available Tools
Work in progress
Geneweaver is in the process of repackaging its tools. This documentation is here for reference, based on existing and legacy tool packaging, and will be updated as each tool is repackaged.
Complete documentation on the legacy versions of analysis tools can be found in the legacy documentation.
HiSim Graph
The HiSim Graph, short for Hierarchical Similarity Graph, is a tool for grouping functional genomic datasets based on the genes they contain. For example: The user may want to determine what a set of experiments on alcohol preference have in common, and what makes various experiments unique from one another. Alternatively, one may wish to take a large set of studies of related phenomena and identify their shared or distinct substrates. In this situation one may want to know whether there is a shared biological basis for addiction and learning, and if so, what the substrate is. The user might also want to examine studies of a large number of related disorders and determine whether a more appropriate biologically-based classification can be constructed.
The HiSim Graph Tool is designed to address these goals; it presents a tree of hierarchical relationships for a set of input GeneSets. The structure is determined solely from the gene overlaps of every combination of GeneSets.
GeneSet Graph
The GeneSet Graph is designed for the user in need of a partitioned display to illustrate just how tied genes are to one another. For example: a user in need of a GeneSet Graph would look for visual references more than chemical references or references by utility. A GeneSet Graph can also help pick apart the most valuable or most occurring genes depending on the user’s preference.
Jaccard Similarity
The Jaccard Similarity Tool displays a matrix of Venn diagrams, which can be very useful for quickly finding overlapping GeneSets and evaluating the similarity of results across a collection of experiments. This snapshot may enable you to determine which can be removed or kept for more complex comparison analysis (such as the HiSim Graph).
GeneSet Clustering
Clustering is one of the most powerful tools in bioinformatics, where classifications are too strict for data distinction, clustering helps give the user an evaluation that is not so distinct.
MSET (Modular Single-Set Enrichment Tool)
Modular single-set enrichment tool (MSET): randomization-based test for list over- or under-representation
MSET was developed to compare gene lists. From four character lists
gene_list1,
gene_list2,
background1,
background2
gene_list1 and gene_list2 is underexpressed or overexpressed relative to
randomness alone.
MSET is based on work from Eisinger et al., 2013, “Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism.” BMC Neuroscience.
ABBA Gene Search
Given a set of interesting genes, do other genes have similar relationships to known sets of genes? For example, given a set of genes known to be related to drug abuse, what other genes share similar expression patterns in drug abuse gene sets?
By answering this question, it becomes possible to elucidate under-studied or obfuscated genes that may play a role in complex phenotypes.
We have developed a new GeneWeaver tool to address this question, which we call Anchored Biclique of Biomolecular Associations (ABBA). This tool takes advantage of the large number of collected data and cross-species integration to find new genes for investigation.
The search begins with a user-provided list of genes of interest, such as highly-studied genes with known pathways and relationships. The database then finds any gene sets that contain at least N of the genes in the provided list. From the resulting list of gene sets, ABBA then isolates any genes that occur in at least M GeneSets but not in the initial list. These resulting genes share similar gene set overlap with the original input set, but may not have been previously considered in relation to the gene set of interest.
Boolean Algebra
The Boolean Algebra Tool performs basic set operations on at least two Gene Sets. Results are displayed as lists of genes beloging to one of the three different types of set operations: Union, Intersect, and Symmetric Difference. Furthermore, results allow users to quickly determine new relationships between Gene Sets and create a new Gene Set based on set-derived findings.
DBSCAN Gene Clustering
DBSCAN (Density-Based Spatial Clustering of Application with Noise) is a clustering algorithm that groups genes into clusters based on how closely related the genes are.
Why Use the DBSCAN Tool?
In general, clustering is used to find patterns or outliers within data sets. In this implementation of DBSCAN, genes in the same cluster would be considered similar, while genes in different clusters would be less similar. An explanation of DBSCAN can be found here. Within Geneweaver, this tool can be used to infer relationships between genes. For example, if clusters with similar genes continue to appear in tests across multiple data sets, one could say that these genes are closely related.