YETI

About YETI

YETI (Your Evidence Tailored Integration) was created by the Laboratory for Bioinformatics and Functional Genomics in the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

Our method relies on a library of 237 Bayesian functional networks, each capturing the biology of a particular pathway or process. In aggregate, this functional library maps the entire human functional landscape. These genome-wide networks were constructed by integrating the growing public data compendium, currently including 35,300 experiments.

Based on the user's dataset, YETI selects relevant pathway/process networks by solving a global regularized linear regression problem. The resulting dataset-specific functional network represents an unbiased study of the user’s experiment that leverages the collection of public biological data.

Reference

Manuscript in preparation

Contact info

In case of questions please contact: FunctionLab

Dataset-relevant biological processes

To generate a genome-wide functional network specific to the user dataset, YETI identifies relevant biological processes (i.e. GO terms) out of a library of 237 distinct biological processes. These dataset-relevant biological processes are clustered into distinct groups based on their gene co-annotations. These distinct groups represent the high-level overview of the functional specificity identified by YETI.

Input file format

The input file format is a tab-delimited text file consisting of the name/ID of the experiment (e.g. GEO's GSM IDs) as columan names, the gene identifiers (e.g. NCBI Entrez IDs) as row names, and the numerical values of the dataset.
The file looks like this:
ID      GSM84590        GSM84602        GSM84603        GSM84604
10998   8.337000        8.395000        8.380000        8.273000
23658   9.180667        9.078667        9.007999        8.829333
23659   6.861000        6.953000        6.832000        6.772000
...
Click here to see an example input file that you can use for YETI integration.