Tips for Interpreting Gene or Protein Interaction Networks \\
Preface
The ability to interpret gene or protein interaction networks is becoming a valuable skill in biomedical research. It is useful not just to those who produce or analyze high throughput array data, but also to those who produce data from single-read-out biochemical assays such as RT-PCR, western blotting, and protein-protein interaction assays. This is because network analysis programs can be used to predict interaction partners, based on the results from multiple single-read-out assays.
This book is meant to be a resource for those who seek to understand what a network of genes or proteins means in terms of the biological processes that are at work in their high throughput data. It will certainly be of help to those who are just starting out, but may also be insightful to veterans of this trade.
This book does not cover the myriad methods and nuances of computing high throughput gene or protein expression data, but is primarily concerned with interpreting the network of genes or proteins that are generated from the computation. Computing the data and interpreting the data harbor their own challenges. My favorite part is interpreting the networks, which I hope you will learn to like after reading this book. As you will see, successful interpretation of gene and protein networks relies more on your knowledge of biology than it does your knowledge of bioinformatics.
This book contains two chapters. Preceding the first chapter is a page that lists the tips for interpreting gene or protein networks. This was meant to be a cheat sheet to help you quickly reference the tips as you do your analysis. Chapter 1 provides what I consider to be the minimal background information to help someone who is just starting out with network interpretations to best understand the tips. This book was written for someone with a graduate level of biology knowledge. Chapter 2 contains 18 tips, each with a short explanation that will help you interpret interaction networks. I have divided the tips into categories that will help you understand their general purpose. The appendix contains a non-exhaustive list of free online software programs that I have found useful in my own research.
About The Author
David H. Nguyen, PhD, is Principal Investigator at the Tissue Spatial Geometrics Lab (www.TSG-Lab.org). He creates pattern recognition algorithms to measure shape features that are hidden to the human eye. He suffers from a fictitious disease called “randomnesia,” which is characterized by the inability to see randomness, only varying degrees of disorder -- a disorder that can be precisely quantified assuming we can measure the order from which the said disorder deviates. By reading this paragraph, you might have just gotten infected with randomnesia.