Keywords:
GeneWalk, Functional analysis, Differential expression, Machine learning, Network representation learning, Graph representation learning, INDRA (Integrated Network and Dynamical Reasoning Assembler), Pathway Commons, Gene Ontology
Poster:
- Robert Ietswaart, Harvard Medical School,
- Benjamin M. Gyori, Harvard Medical School,
- John A. Bachman, Harvard Medical School,
- Peter K. Sorger, Harvard Medical School,
- L. Stirling Churchman, Harvard Medical School,
Short Abstract:
A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk (github.com/churchmanlab/genewalk) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.
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Keywords:
gene expression, biomarker discovery, neurological diseases, network analysis
Poster:
- Gerda Cristal Villalba SIlva, Federal University of Rio Grande do Sul,
- Ursula Matte, Federal University of Rio Grande do Sul, Postgraduate Program in Genetics and Molecular Biology,
Short Abstract:
Mucopolysaccharidoses are lysosomal storage diseases involved in glycosaminoglycan degradation. Neurological damage is present in seven MPS types. The aims of this work were to identify potential biomarkers for neurological impairment in different types of MPS. The most relevant proteins in the networks and ontology terms related to neurological damage in MPS were identified and compared among diseases. We performed the clustering analysis for GSE111906(MPSI), GSE95224 (MPSII), GSE23075 (MPSIIIB), GSE15758 (MPSIIIB), and GSE76283 (MPSVII). Besides, we develop a user-friendly platform call MPSBase . Regarding biomarker discovery analysis, the top 10 genes were ranked according to the maximal clique centrality. For instance, for MPS I, the related ontologies wereclathrin derived vesicle budding and clathrin-mediated endocytosis. MPS II top genes were related to brain renin-angiotensin system. MPS IIIB top genes comprisesthe ontologies acetylation and chromatin regulation. The MPS IIIB top 10 genes were related to the activation of the mRNA cap-binding complex. Finally, MPS VIItop genes were present in the ontologies C-MYB transcription factor network. Moreover, we identified several immune system processes like adaptive and innateimmune systems, Class I MHC mediated antigen processing and presentation; and activated TLR4 signaling across the different MPS types. Ontologies present in all the MPS , like axon guidance, Calcium signaling, PI3K-Akt, and Wnt signaling pathway. We hypothesize that these pathways are deranged because glycosaminoglycans play an essential role in the ECM, helping to regulate several processes. Systems biology approaches may help to understand the mechanisms of neuropathology in the different types of Mucopolysaccharidoses.
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