There is a lot of painting with incredibly broad brushes in this article. Even making a generalization about the life sciences is such an over broad scope that it's ridiculous. Most life sciences people I know are different sorts of macrobiology researchers doing really interesting research and field work - they have their complaints, but very different ones than what's expressed in the article.
There is, but his description of the research process seems accurate at least for my partner (currently also in a structural biology lab for his PhD) whom I showed the blog to. In particular this depressing paragraph:
>Doing experiments is extremely tedious and usually trivial. Once you become familiar with some experiments, you do those experiments routinely and hardly learn anything new. Doing life science experiments requires extremely high concentration. If you made an error during experiment, say preparing a bottle of solution with incorrect concentration of some components, it could hardly be traced back. Your experiment results will thus be wrong and irreproducible. Some experiments cannot be fully automated by advanced instruments, and they require good hands to do fine operations. If you don’t have good hands, usually your experiment results would be inconsistent and untrustworthy. Experiments would usually take extremely long time to conduct. Unlike computer programs, they usually could not be “saved” in halfway. Many experiment materials and samples are fragile, sensitive to environments, such as temperature and light, and they have their “life cycles” as well. This means that the fresh experiment materials and samples should be used as soon as possible to ensure their quality. The same experiment material or sample is likely different to what it was two hours ago. If somehow you realize that anything went wrong in the experiments, usually you would have to start from scratch again, whereas for computer programs you could always start from somewhere in the middle as long as you saved it. This further means that your life will be managed by experiments and you will no longer have control over your own life. For example, once you started a scheduled 24-hour experiment, you would have to follow exactly to the experiment plan. If you scheduled to do something for the experiment, even if it was at 2:00 AM and there was storm outside, you would still need to show up in the lab and start to do experiment on time. Otherwise, the whole experiment might screw up, and you would need to restart the whole experiment on another day.
highlights a lot of the inefficiencies in doing lab research. The current setup of labs with PIs who own the resources and decide what experiments get done, seems to waste a lot of intellectual potential of grad students and postdocs. And also holding up this edifice is many, many undergrads and research technicians who exchange cheap labor for resume-building on their way to medical school and grad school applications.
Obviously there are advances happening in the field, but it clearly wasn't right for the author, or anyone who's not willing to play the game.
Note thought that nothing in that complaint was specific to life sciences.
It could apply to chemistry, or physics, or any other lab-centric field.
Furthermore, not all of the life sciences are lab based. Population genetics, for example, was "[t]raditionally a highly mathematical discipline" quoting Wikipedia, and a lot of bioinformatics is computer based.
Which means that quoted text doesn't really support the thesis.
In one of the earlier threads I commented, at https://news.ycombinator.com/item?id=21987596 , that "It reads like it was written by a disgruntled and undereducated undergraduate frustrated with biology classes and venting."
The author does not understand what science is, and makes statements which are outright wrong.
See my comment for details of some of those wrong statements.
,, Please remove life science or any interdisciplinary majors related to life science from college''
This is crazy. Finally with Illumina output data sets life science gets much closer to machine learning, and I see more and more open articles with open downloadable data sets, so the fun for us with data mining background is just getting started.
The field is changing fast, and maybe the more important message should be to have a good understanding of statistics (and using R), but throwing away a successful field is not a solution.
While I agree that collecting more data opens up the possibility for more complex, exciting analysis, gene expression data is no magic bullet. It is often noisy and still prone to experimental error because biological systems are so heterogeneous. Also, for a lot of investigations, there are only a handful of samples which means that the number of distinct data points for a specific transcript are very low. You typically end up with many columns but few rows in your dataset which makes modeling biological systems quite challenging.
As far as I see the current solution to the ,,few rows'' problem and the read errors is to have even more data and to make sure that each read is treated as what it is: a sample from a different cell.
This way for example a bone marrow sample can be used to model the whole dynamic that's going on in the human system, and the evolution of cells / RNA expressions between cells. Of course this means that more probabilistic hidden models have to be used, instead of putting together the reads to form a genome.
The way cancer is studied is also changed by this method, though you 'll need the new Illumina machine with 30x number of reads that was announced a few days ago.
