Software Carpentry Course

35 students took new mini-course in software carpentry

Tuesday 13 Sep 16
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Contact

Nikolaus Sonnenschein
Senior Researcher
DTU Biosustain
+4521 79 89 22

Contact

Anne Wärme Lykke
Communications Officer
DTU Biosustain
+4521 12 37 70

With a new mini-course, students can now get a taste of what it takes to become a skilled ‘software carpenter’ and to do advanced computational metabolic engineering.

Thousands of gigabytes of data are generated every day in life sciences. For instance, data about DNA sequences, RNA sequences, or gene expression. But with data generation becoming ever more easy in biology, life scientists and engineers are facing challenges in analyzing data in their line of work.

 

Therefore, DTU Biosustain and DTU Bioengineering put together a mini-course that was held for the first time from 5–8 September with two days of so-called software carpentry followed by two days of computer-guided cell factory design.

 

Tools are essential for data analysis

The course addresses Master students, PhD students, and Postdocs, and the aim is to enable the students to do advanced computer modelling on data and on cell metabolism.

 

“Recently it has become clearer to us, that most biotechnologists have a hard time analyzing their data and automatizing repetitive tasks, because they don’t usually receive much training in data-analysis and scientific programming,” says one of the course leaders, researcher Nikolaus Sonnenschein from DTU Biosustain. He continues:

 

“Without the proper tools and training, even simple bioinformatics tasks can become a huge time sink, because students copy-paste information into web interfaces, instead of processing their data in batch.”

 

Computational biology 

 

Don’t reinvent the wheel

35 students attended – quite impressive, considering that this was a new course.

 

The first two days were about programming and software. Here the students learned basic computational skills that specifically target life science tasks such as task automatization, command line usage, programming with Python, and data analysis in R.

"I will definitely recommend this course to other students working with cell factories"
Christine Skovbjerg, PhD-student and course participant

 

The last to days were about genome-scale metabolic modeling with a focus on cell factory engineering covering computation of medium and growth dependent product yields, prediction of essential genes, identification of shortest production pathways, and prediction of gene deletion and over-expression strategies.

 

One of the students was Christine Skovbjerg. She has just started her PhD on cell factories, and she liked both the introduction to programming, but also the more concrete tools:

 

“It was nice to be introduced to the cell factory specific tools. Now I know where to look for scripts, if I for instance want to do some knock-outs and predict how they affect the cell, or if I need to make a specific graph from my data. Which is nice, because you don’t want to reinvent the wheel every time you want to do an experiment or data analysis,” she says, and concludes:

 

“I will definitely recommend this course to other students working with cell factories.”

 

About the course

  • The course is called Scientific Computing for Life Scientists and Metabolic Modeling for Cell Factory Design (Course number 27824 in DTU’s course database) and gives 2.5 ECTS points. 
  • According to plan, the course will be held once a year onwards. Furthermore, weekly meetings are held to enable the students to get the learned skills into practice. Please contact Nikolaus Sonnenschein (niso@biosustain.dtu.dk) if you are interested in joining these meetings. 
  • Approximately 10 teachers where helping the 35 students at the course.
  • The course is offered in collaboration with Software Carpentry – an organization that has been teaching basic lab skills for research computing to scientists and engineers since 1988.
  • To pass the students will have to hand in reports that demonstrate the successful application of the tools and methods covered in the course (4 weeks).

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