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This version constructs a PPI of gene products modified by the ubiquitin-like interferon-stimulated gene 15 (ISG15) connected to the associated enriched GO-terms acyclic graph using NetworkX.

GOwalk receives published proteomic data sets and performs random walks using node2vec generating latent representations of how each gene in the list relates to one another, and to their overall function.

The goal is to provide contextual insights for high-throughput data, offering important descriptions that help guide decision-making in research.

Whereas reductionism has yielded splendid results in science, there is an important sense in which it is artificial, and in this sense false. By starting from wholes and moving ‘down’ into parts, one is moving in the opposite direction from the way matters arise. Ursula Goodenough, The Sacred Emergence of Nature

Access to higher dimensional insights: machine-readable ontologies are used to identify interesting patterns and associations among your genes.

Emergene provides some cross-compatibility with R's bioconductor: This work is heavily influenced by tools such as GoSemSim, RRVGO, and g:Profiler.

from enrich import *

name = 'project_name'
file = 'path_to_genes.csv'

data = Enrichment(name, file)   # automatically creates project directory

Once the object is initialized, you can view various attributes such as:

data.name   # project name
data.genes  # straight forward
data.pairwise   # PPIs from STRINGdb
data.network    # networkx representation
data.sub_network    # dict of spectral clusters as cluster:gene sublist pairs

data.get_gprofiler()    # stores enrichment data from gprofiler as data.enrichment
data.get_semantics(ont='BP, MF, or CC')    # returns a similarity matrix (sem_sim) and semantically reduced df (sem_red)

data.enrichment
data.sem_sim
data.sem_red

In progress

• Notebook format
• Shell compatibility
• Semantic similarity analysis
• Semantic reduction
• Class with visualization methods
• Save states for easy re-loading
• More species