Series 3 – Talk to Dr. Gavin Chapman from the Victor Chang Cardiac Research Institute
By Jianzhou Zhao
Intersect and NCI continue to meet attendees of our digital research training program partnership and seek their feedback. This month, we are excited to hear from Dr. Gavin Chapman, who is a developmental biologist at the Victor Chang Cardiac Research Institute (VCCRI). Gavin’s research is focused on understanding the genetic causes of malformations and he is now applying human genomics to identify genes that, when mutated, cause malformations such as congenital heart disease.
Gavin is using the Lightweight Analysis of Morphological Abnormalities, or LAMA – an automated pipeline for phenotyping mouse embryos. The LAMA pipeline is an easy way of producing volumetric data and provides statistical analysis. Researchers need to use Python to work with LAMA and initially, they were using VCCRI’s in-house facility for data processing. The increasingly large amount of data generated has required them to implement the LAMA pipeline on NCI’s supercomputing infrastructure in order to take advantage of the parallel computing power.
Gavin participated in the Learn to Program: Python and Getting started with HPC using PBS Pro courses and found them extremely useful. Gavin said, “The HPC course is so relevant to what I am trying to do and I appreciate the course material. It is a nice supplement to the documentation on the NCI Gadi. In terms of data manipulation with pandas in Python, since I did not have much background in that, it was really good to learn what it is capable of. I have realised that I can use pandas to manipulate the data frames that are generated by LAMA.”
Gavin has highlighted how impactful and beneficial the machine learning courses would be for their research.
“We aim to use machine learning to support the recognition of embryos that are terribly malformed because LAMA is not really good at doing it. I have tried to learn Linear Regression in books but I haven’t learned much. By contrast, the same concepts were explained so well in the Intersect/NCI courses.”
When asked about the main reason he returned to attend the courses, Gavin said that, in the training, he was able to get a clear understanding of what the language or packages are capable of in a relatively short time frame. It is very different from some self-paced courses where people have to invest a lot of time into it. Gavin also mentioned that our course material is well developed and is an all-in-one reference with research-related examples, which normally are difficult to find online.
This interview and article are jointly prepared by NCI and Intersect.