I3:A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data
- Author:
Tan YUN
1
;
Jiang LULU
;
Wang KANKAN
;
Fang HAI
Author Information
1. State Key Laboratory of Medical Genomics and Shanghai Institute of Hematology
- Keywords:
Self-organising;
Human genetics;
Interpretation;
Evolution;
Machine learning
- From:
Genomics, Proteomics & Bioinformatics
2019;17(5):503-510
- CountryChina
- Language:Chinese
-
Abstract:
We propose a computational workflow (I3) for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle. We illustrate the use in inter-preting genetics of gene expression and understanding genetic regulators of protein phenotypes, particularly in conjunction with information from human population genetics and/or evolutionary history of human genes. We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of gene expression in brains, and if highly expressed, have broad effects on the protein phenotypes studied. We suggest that this workflow presents a general solution to the challenge of complex genetic data interpretation. I3 is available at http://suprahex.r-forge.r-pro-ject.org/I3.html.
