computer science course for genetic engineering | Coursera Community
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computer science course for genetic engineering

  • 28 July 2019
  • 6 replies

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Hi, I'm a 3rd year student at cellular and molecular biology specialist program, I find I need some knowledge about web programming, dynamic image generation, web-based interfaces and database design to complete some research related to genetic engineering. But it seems that the CS minor program in my university cannot meet these needs. If anyone can help me in finding related course on coursera I would be very appreciated!

6 replies

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Unfortunately you have not properly explained how you really intend to apply all these skills in Genetic engineering. I have only knowledge on how you can apply it in molecular biology and so my recommendation is based on that.
I think you can try and specializations.

Good luck
I am not sure what topic do you want to do in genetic engineering. It seems that you have not study any web design course. Therefore, you may take a look at this course:
It teaches the basic elements in web design.
Hope it will help you☺
The fastest way to get your genomic and genetic engineering works manifested through web interface is by means of R and Shiny. The course that teaches Shiny is Developing Data Products, a component of Johns Hopkins Data Science Specialization. Programming web interface complete with user interaction widgets is almost a no-brainer using Shiny.

Genomic Data Science is another Johns Hopkins' Specialization which I think is all worthwhile to spend the time and money on it. It uses Python instead of R but it's quite easy to combine these 2. You can do all your genomic stuffs in Python and produce the output as CSV which can then be easily picked up by R. In my work delivering machine learning and analytics to world's larges corporations, this is commonplace. Quite often, data wrangling and processing are done in R and picked up by Python later for production. During proof-of-concept and proposals stages, many prototypes were rapidly done in Shiny to showcase the concept, objectives and outcome. The end product sometimes ended up as the same Shiny apps with improvements. There are times they ended up as Python data products.

If you are in a hurry and can only choose one of the two programming languages, try checking out Bioconductor for Genomic Data Science Bioconductor is a repository that houses R packages for Life Sciences. The standard R repository is located at CRAN.

Apart from Shiny, React JS is one of the most popular web programming frameworks. There's a specialization offered by Hong Kong University of Science and Technology. Flask is also gaining traction in many corporations.

In conclusion, you can consider doing the following:

  1. Learn R, Shiny and Bioconductor all offered by Johns Hopkins to complete your research workflow from start to finish purely using R (fastest and all materials available on Coursera and self-contained)
  2. Mix of R and Python by doing genomics works on Python, web interface using Shiny (slight more time due to 2 programming languages)
  3. Mix of R, Python and other web frameworks such as React JS and Flask (taking the longest time to develop)
Hi Audrey

I am currently a PhD student in Bioinformatics. (computational genomics). I have a degree in computer science and I have worked as software engineer in Silicon Valley for over 10 years on web programming, database design and implementation a lot of other stuff.

Your question is very vague. Hence I imagine most of the answer you are going to take an enormous amount of your time trying to figure out if and how they apply to your specific needs

I would start by trying to write a couple of use cases. A use case describe how a user of your system interacts with the application.

next I would write up a list of product requirements. I would include and technical requirements like It has to be python because that is all I know. Its import to include information about the development schedule. For example I need to start and finish before the end of the qtr. Its also important to understand what level of quality you need. For example if its a class project that you are not going to use again then the implementation can be simpler than something that is going to be used by other and deployed on a public server

One the database side. Its really important to unders what the data looks like, its size, and how it will most often be accessed. based on that you might decide you should use a relational database or maybe no-sql or even just files on disk

At this point you are prepared to ask good questions and as such will hopefully get better answer. You might even be able to find a C.S. intern looking for a senior project