Can bioinformatics be more accessible?
You may have heard about the wonderful things you can find in the raw data your lab produces. Some of it gets published: it becomes part of a groundbreaking study, or inspires the development of a life-saving drug, though most of it ends up sitting on a hard drive. While this data provides an unprecedented level of detail about the molecular mechanisms associated with disease, the generated datasets are huge, of variable quality, and are very complex…
Learning some basic bioinformatics skills can empower biologists to make use of their own data: they, after all have the best understanding of biological processes. However, because the field is advancing more quickly than traditional biologists can integrate evolving data science analysis methods, many labs outsource the work. This introduces unnecessary inefficiency, and opportunity for error in clinical research.
Over the past few months, our team has been working on courses and projects that we hope can make bioinformatics more accessible to biologists, computer scientists, and everyone in between. Data analytics skills are in high demand in clinical research and treatment development, though most bioinformatics education courses focus on technical issues rather than the bigger picture of big data and its potential to change the way we view disease and treatment discovery. The result is an approach to bioinformatics which ignores biology.
Our courses contain projects crafted using open source data contain detailed biological information, broken down into digestible, easy-to-manipulate visualizations, offering scientists and students alike a new means of working with ‘omics data — without expensive the technology and coursework.
Introducing…. (drumroll) T-BioInfo in Education
As we get ready to launch, we need you to help millions of non-bioinformaticians get their hands on the wonderful public domain data that has been collecting dust at the NIH servers. For now, users are invited to test our two courses, “Introduction to the Power of Big Data and Bioinformatics” and “Transcriptomics”.
What needs testing:
We want to make sure that the simple technical aspects of our site function properly for our users, such as loading pages, playing videos, completing quizzes, navigating between pages, etc. We also want to that the concepts are clear to users and one can follow the course as it develops.
We will use the tester’s comments for course optimization, integrating as much feedback as possible into the design of our program. We are seeking feedback on technical bugs, course content, and are open to suggestions for improvements. After taking our courses, kindly record any information you would like to provide in our user survey here.
If you know others seeking skills development in bioinformatics and data analysis, please forward our initiative. We welcome all who are interested to join our team of beta testers.
Why you (and your friends) should care:
This is not just about science. Bioinformatics is an interdisciplinary approach, which traditionally necessitates advanced knowledge in biology, computer science, statistical methods and mathematical models. Bioinformaticians, or “Data Scientists” are highly sought after by employers data scientists pull in hefty salaries for their expertise. The average annual wage for a U.S. bioscience worker reached $94,543 in 2014, a whopping $43,000 greater, on average, than the overall U.S. private sector salary(1).
The market itself is booming, with an expected growth of 21.2% between 2014 and 2020. According to a 2016 report from the BIO International Convention, the U.S. bioscience industry employs 1.66 million people, a figure that includes nearly 147,000 high-paying jobs created since 2001(2). There is an urgent need for quick, cost-effective, and accurate data analysis in the field of biomedical research as well.
Russ Altman of the Altman Lab at Stanford put it this way:
“Now there are these amazing data sets from extremely clever experimentalists who’ve figured out how to do things in high-throughput [experimentation], and they represent a substantial challenge to people who aren’t trained in computation because it passes what I call the ‘Excel barrier,’” he says. “I’ve been amazed at what a biologist with Excel can do, but we have now exceeded the Excel barrier in terms of the number of rows and columns and the computational powers of Excel(3).”
Let’s get started — our two introductory courses free are available free to any registered user. Also, if you have an interesting dataset we should consider, please drop us a line at info@pine-biotech.com
1. Shields, Debbie. “Bioinformatics Market by Technology and Services and Application — Global Opportunity Analysis and Industry Forecast”, 2013–2020. Allied Market Research. 8 Aug. 2017. Web. May 2014.
2. “National Bioscience Report Shows Industry Creating Jobs and Driving Innovation.” Biotechnology Innovation Organization. 7 Aug. 2017. Web. 7 June 2016.
3. Prince, Michael. “Computational Biologists: The Next Pharma Scientists?” Sciencemag.org. Science Magazine, 7 Aug. 2017. Web. 13 April 2012.
