Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.
High-throughput, 'omic' methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Nurcan
N
Tuncbag
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Sara J C
SJ
Gosline
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Amanda
http://orcid.org/0000-0001-6156-5046
A
Kedaigle
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Anthony R
http://orcid.org/0000-0002-3394-1275
AR
Soltis
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Anthony
http://orcid.org/0000-0002-5324-9833
A
Gitter
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Ernest
http://orcid.org/0000-0001-9249-8181
E
Fraenkel
D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data*; D044127:Epigenesis, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism; D060066:Protein Interaction Maps / Q000235:genetics; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors