Since the completion of the Human Genome Project, there has been a natural surge in biomedical research aimed at gene discovery. Using genome-wide association studies (GWAS), bioinformatics, and other approaches, this process has focused largely on determining what genes are implicated in specific diseases.
While this process may shed light on what genes are involved in diseases, it has done little to help investigators understand why a gene or genomic region may be involved in a disease or a disease risk. A new Research Affinity Group at CHOP Research aims to take the findings from various types of studies, including GWAS, a step further in order to understand entire networks underpinning disease susceptibility.
At the heart of the DNA-Protein Interaction Research Affinity Group is the analysis of data derived from chromatin immunoprecipitation coupled with high-throughput sequencing. More commonly referred to as ChIP-seq, the technique is used to look at how proteins like transcription factors interact with DNA, and to find the regions of the genome these transcription factors occupy to control gene expression.
The DNA-Protein Interaction research affinity group, co-led by Struan Grant, PhD, of the Department of Pediatrics and Genetics, and Andrew Wells, PhD, of the Department of Pathology, will deal with the processing and analyses of ChIP-seq and related data to gain insight into disease networks, identify vulnerabilities in given networks, and look for points amenable to therapeutic intervention.
“The new affinity group is a forum for investigators with diverse backgrounds and expertise to learn the downstream effect of transcription factors and related DNA binding proteins,” says Dr. Grant. “We can now work to analyze and translate the numerous findings made from GWAS and elsewhere — a natural step in discovering not only what causes disease but why.”
“The sequencing of genomic information is not the bottleneck — it’s the analysis,” says Dr. Wells. “The DNA-Protein Interaction affinity group creates a community that can benefit from what we can learn about disease networks through ChIP-seq and related techniques, and then develop a pipeline to find novel ways to understand and target those diseases.”