New biomedical techniques, like next-generation genome sequencing, are creating vast amounts of data and transforming the scientific landscape. Theyâre leading to unimaginable breakthroughs â but leaving researchers racing to keep up.Â In this feature for Mosaic, Tom Chivers meets the biologists âÂ junior and senior â who are learning to work with algorithms.
If you want to dive deeper into this topic, hereâs some further reading. Weâve broken things down into key subtopics, but otherwise these links arenât listed in any particular order â so feel free to dip in and out.
Big data in research â an overview
Emily Dreyfussâs articleÂ âWant to make it as a biologist? Better learn to codeâÂ reflects on the limitations facing expert researchers who were never taught programming skills.
ThisÂ 2014Â ForbesÂ articleÂ anticipates how data might transform science: âIt is as if we are back in the days of Leeuwenhoek, staring into a microscope for the first time and just beginning to understand the possibilities.
Wet and dry labs
This 2017 studyÂ compares studentsâ perceptions of wet and dry labs. The results, after comparing two experiments, found that students considered the wet-lab work to be more like âreal scienceâ, although they found âmore scientific tasksâ to be involved in the database work.
David Heckermanâs 2012 speechÂ âBiology: from wet to dryâÂ details how biology has progressed from using one-off experiments that generated small amounts of data to having so much data readily available that a hypothesis can be tested without any collaboration with a wet lab whatsoever.
InÂ this 2015 interview, Sarah Teichmann, who works at the Wellcome Sanger Institute, explains how she combines computational methods with lab experimentation.
InÂ Tom Chiversâs article for Mosaic, Professor Gil McVean observes that research labs 15 years ago were typically 90 per cent wet but are today 90 per cent computing. This has resulted in excess empty research space. For instance, theÂ Greater Baton Rouge Business Report recently announcedÂ that 16,000 square feet of underutilised wet-lab space at Louisiana State University would be sold and converted into office space.Â
Early criticisms and challenges
The 2013 articleÂ âWhy big data is bad for scienceâÂ claims that big data samples take longer to analyse, with an increased risk of spurious correlations or flukes.
This paper from 2008Â explores the difficulties of daily cooperation between wet and dry researchers, in what is dubbed âthe moist zoneâ.
Timo Hannay, former managing director of Digital Science, claimed in 2014 that the world of research was not doing enough to embrace the power of information. Read the details in his articleÂ âScienceâs big data problemâ.
More from Mosaic
Tom Chiversâs article on big data in scienceÂ concludes our season on genomics, which has coincided with the 25th anniversary of the opening of the Wellcome Sanger Institute.Â Visit our genomics pageÂ for more information about the Sanger Institute and the impact it has had on science and healthcare.
Here are two other highlights from the series, both reporting on Sanger Institute projects that take a big data approach to science.