Dr. Raman Babu is a Maize Molecular Breeder at the International Maize and Wheat Improvement Center (CIMMYT). Like his counterparts conducting human genetic research, Babu used to rely entirely on free, open-source tools to complete his work. Frustrated with continual crashes and technology that was developed in the pre-SNP era, Babu switched to SNP & Variation Suite (SVS) almost a year ago. Here’s a bit about his experience.
Jessica: Can you tell us a little about your research?
Babu: My job at CIMMYT is primarily focused on molecular marker technology to develop varieties of maize with improved traits that farmers can grow in their field. In maize we are particularly interested in drought tolerance, nitrogen use efficiency, nutritional traits such as pro-vitamin B and protein quality in terms of amino acid balance, and resistance to the most prevalent diseases in Africa, Asia, and Latin America.
Jessica: How do you develop improved maize varieties?
Babu: Until about two years ago, we had been extensively using simple sequence repeat markers (SSR markers). Very recently we moved over to SNP markers with the maize genome being sequenced and genotype technologies improving tremendously, leading to reduced cost per data point. Genotyping at high density is quite cheap now.
So the way we do it – we build a training population which is genotyped and phenotyped, called our model population, and based on this, we try to predict the phenotypic performance from molecular market data. In CIMMYT, as well as in plant breeding companies, this is the heart of our of research. Every year we come out with a large number of new lines, and we have a need to predict the phenotypic performance of these without without testing them extensively in the field.
Jessica: How do you analyze the phenotypic information?
Babu: We used to use publicly available software exclusively. But one important issue is that they were developed in the pre-SNP era and were designed to handle only a small number of markers and samples. So when you put in a large number of markers and samples, in most of these softwares, it doesn’t work. They simply hang. That’s where we find SVS to be quite useful. SVS can handle a large amount of data with relative ease, and also it has quite a number of interesting features.
Jessica: Which SVS features are most relevant to your work?
Babu: As I mentioned, the key feature for us is that SVS can handle a very large amount of data. In some instances, it’s one of the only options. The graphics features of SVS are great. Another thing is the haplotype analysis; the haplotype-block definitional algorithms are very useful. Also, SVS consumes less time compared with other software in producing results. And the regression module is quite robust – it has several options to include covariates in models which are used very often. Finally, we are using SVS as a database tool as it is convenient to store the data in the DSF file format.
Jessica: What’s next for CIMMYT?
Babu: We’ve recently started a huge project to sequence all of our accessions – we have close to 35,000 within our gene bank. Additionally, we are moving to a new platform that can produce 500,000-800,000 SNPs for each sample. So, as you can imagine, we don’t have many software platforms that can handle that amount of data. But SVS can.
Read the Case Study featuring Babu on www.goldenhelix.com »
…And that’s my (and Babu’s) 2 SNPs.