Commenting: Genomics in 2011
This article is originally published at https://lcolladotor.github.io/
Today is my first day of classes and Kasper couldn’t have had a better timing to share the link to Genomics in 2011: challenges and opportunities. There, the Editorial Board members of Genome Biology gave their opinion on: important 2011 papers, influential people for their careers, advice to young scientists, top challenges in their field, and unlimited money projects. I felt identified several times and as:
I think one of the most important skills in research is the ability to communicate ideas. […] practicing both writing skills and oral presentation skills.
here I am sharing my view on the paper.
Overall the paper is a good read and I enjoyed most of it, so I highly recommend reading it.
The top papers section is quite broad and it felt to me like a huge abstract on many papers. Now I want to read the paper on “phenologs”, a concept that seems very simple and natural, and has helped understand a bit more what genes do (functional genomics).
The parts about influential people and unlimited money projects are not so interesting. I felt that it was too personal and I didn’t feel that it related to me much. Regardless, I’d like to highlight some parts of the text:> * […] showed me the fun of methodology development in computational biology > * […] emphasized establishing a solid foundation in computer science and statistics but also developed our skills to identify relevant and impactful biological questions > * His humble upbringing, ability to admit when he was wrong, and persistence for what was right has continued to inspire me.
The advice section was very appealing to me and I felt reassured as some of the advice I gave even younger scientists (check my talk to LCG students) popped up. I surely learnt a few things too =)> * Surrounding yourself with people who are smarter than you also helps you raise your game. > * Published is better than perfect. > * Importance of communication > * you need to appreciate the biology as much as, if not more than, the statistical method or computer science > * The success of our field is ultimately measured by the impact we can have on our understanding of biology > * I would say it’s the ability to understand both the experimental and the computational sciences. […] I would learn the key principles in computational biology or at the very least the linguistics, and vice versa.
I hadn’t taken consciousness of the fact that the amount of computing you can do (per dollar) is doubling every year while the size of genomic databases is going up by a factor of 10. I knew something was off and that we are getting more data, but I didn’t know the ratio. Plus, I have to agree with the comment that cloud computing is not going to be the solution though I’m not sure if:
The only solution is to discover fundamentally better algorithms for processing these databases.
Sure, we need better algorithms, but I think that it’s fair to ask for cleaner and higher-quality data. This doesn’t mean that the data format has to be simpler, as:
Far too often, enough biological details are abstracted away so that the solution loses its biological relevance.
Sad, but true.
I do agree with:
Another challenge is to educate people about genomics and to tone down the natural hype of the genomics field.
Just as with any discipline, it’s not easy to explain your field to a random person and it’s a harder job when someone exaggerates a set of results.
The argument that RO1 NIH grants are not built for young genomics scientists is interesting and I do hope that it changes soon. Though I don’t have a clue as to how the following is going to happen:> the last challenge is to transform the academic review system in our institutions > Understanding the systems-level ecological rules governing microbial community structure<
surely opened my eyes as it was one of my main interests a few years ago (and stills interests me ^^).> streamlining methods for turning next-generation data into actionable biology
sounds very fancy!! It’s as fancy, and important, as developing new ways to visualize networks that take into account time instead of viewing all the possibilities at once.
To finish off the post, I agree completely with:>I believe the biggest bottleneck is the bioinformatics and the shortage of researchers in the field. There needs to be a big investment to address this shortage.
Note that I think that there are lots of bioinformatics-converts: people who recently joined the field to fill in the gap in their lab. As:>In my opinion, too many computational biology researchers are working in isolation on marginally relevant problems or making incremental improvements in areas that have already been well-populated by methods that are already adequate.
So, yes, we need more bioinformaticians as most labs have high-throughput experiments (with only one experiment you already need a bioinformatician), but I do hope that a good proportion of them are trained to develop methods and focus on important problems and new areas instead of marginally relevant ones on “old” areas.
That’s why I’m studying a PhD in Biostatistics!
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