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A farewell to bioinformatics (2012)

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Re: A farewell to bioinformatics (2012)

#111

Post by ginger_beer »

Inufu wrote: post #96
chrisamiller wrote: post #62

Some thoughts on this article: - This guy clearly has a limited understanding of the field. This quote is laughable: "There are only two computationally difficult problems in bioinformatics, sequence alignment and phylogenetic tree construction." - As a bioinformatician, I feel sorry for this guy. Just like any other field, there are shitty places to work. If I was stuck in a lab where a demanding PI with no computer…

Out of curiosity, what other computationally difficult problems are there? I'm very interested in bioinformatics, but sadly don't know as much about the field as I'd like.

I do PhD research in metabolomics -- one of the latest omics in bioinfo-- with the CS department in my university. At the moment, we're working on alignment and identification of metabolite data. The data is not big in the sense of genomics data, but messy and complex due to the nature of the instruments (mass spectrometer), which will not get better THAT much in the foreseeable future.

Definitely a computationally difficult problem because while naive approaches work, they produce crappy results, wasting the result of tens of thousands of dollars of experiments. I see a big move towards applying statistical/machine learning methods, and graph theory stuffs in our field.

A lot of the rants in the original article are correct, with regards to prototyping and throwaway codes. That's because researchers are rushing to get an MVP out. The truly good ones got turned into (usually open-source) products, where the code quality hopefully improves a fair bit.

If you're a CS person who's interested or considering a move into bioinfo, I wrote a blog post about it recently: http://www.joewandy.com/2013/01/getting-into-bioinformatics....

Re: A farewell to bioinformatics (2012)

#112

Post by CrLf »

"I’m leaving bioinformatics to go work at a software company [...]"

"[bioinformatics] software is written to be inefficient, to use memory poorly, and the cry goes up for bigger, faster machines! [...]"

Well, the author is heading for a very bitter surprise...

Re: A farewell to bioinformatics (2012)

#113

Post by [deleted] »

epistasis wrote: post #37
michaelhoffman wrote: post #21

Earlier quoted context omitted.

Hi, I'm a bioinformatics researcher. Apparently I work for this guy's ex(?)-employer although I have never heard of him before. My single most limited resource is programmer time. My time and the time of other people who work with me. I have access to loads of computers that sit idle all the time, even if it is on nights and weekends. There is zero opportunity cost to me in using these computers more fully. I have en…

>There can be a big opportunity cost in trying to rework a workflow so that it is more efficient and then test it thoroughly ensure correctness. Hi, I recognize your name as a legit bioinformatician, am a huge fan of the lab that you're currently in, and others should listen to you. I'd like to add that for many projects, general reusable software engineering is not necessarily a huge advantage. Instead of verifying…

[deleted]

Re: A farewell to bioinformatics (2012)

#114

Post by lemming »

jstevens85 wrote: post #83
lemming wrote: post #46

This is a little discouraging - BioInformatics was my top choice for a Master's program I'm planning to start this year. The program at Melbourne Uni looks really good (accepts from three streams, Math/Stats, Biology or Computing and tailors the course based on your background). Maybe I should go for a more generic Machine Learning one and try to apply that to healthcare in some other field if things are really this…

Could you add an email to your profile? I'd like to email you regarding Masters courses at UniMelb.

Sure, done.

Re: A farewell to bioinformatics (2012)

#115

Post by lemming »

chrisamiller wrote: post #66
lemming wrote: post #46

This is a little discouraging - BioInformatics was my top choice for a Master's program I'm planning to start this year. The program at Melbourne Uni looks really good (accepts from three streams, Math/Stats, Biology or Computing and tailors the course based on your background). Maybe I should go for a more generic Machine Learning one and try to apply that to healthcare in some other field if things are really this…

As someone in the field, let me assure you: This article does not accurately reflect the state of the field.

Thanks for the reply. I wasn't basing this just on the article, there seem to be a fair number of comments here supporting a less-extreme version of what he's saying.

Re: A farewell to bioinformatics (2012)

#116

Post by ascotan »

Having working in the bioinformatics industry as an SE for 9 years I can both agree and disagree.

1. I agree that SE standards and good coding practice are completely absent in the bioinformatics world. I remember being asked to improved the speed of some sequence alignment tools and realized that the source code was originally Delphi that had been run through a C++ converter. No comments, single monolithic file. The vast majority of the bioinformatics code I worked with was poorly written/documented Perl. In addition a lot of bioinformatics guys don't understand SE process and so rather than having a coordinated engineering effort, you end up with a lot of "coyboy coding" with guys writing the same thing over and over.

2. I agree that productivity is very slow. This is a side product of research itself though. In the "real world" (quoted) where people need to sell software, time is the enemy. It's important to work together quickly to get a good product to market. In the research world, you get a 2/5 year grants and no one seems have much of a fire under them to get anything done (Hey we're good for 5 years!). You would think that the people would be motivated to cure caner quickly (etc), but it's not really the case. Research moves at a snail's pace - and consequently the productivity expectations of the bioinformatics group.

3. I disagree that research results from the scientists are garbage. Yes it's true that some experiments get screwed up. However, if you having a lot of people running those experiments over and over, the bad experiments clearly become outliers. Replication in the scientific community is good because it protects against bad data this way. Somehow the author must have had a particularly bad experience.

4. Something the author didn't mention that I think is important to understand: most scientists have no idea how to utilize software engineering resources. The pure biologists, many times are the boss, and don't really understand how to run a software division like bioinformatics. Many times PHD's in CS run a bioinformatics group, who have never worked in industry and don't know anything about good SE practice or how to run a software project. A lot of the problems in the bioinformatics industry is directly related to poor management. Wherever you go you're going to have team members that have trouble programming, trouble with their work ethic, trouble with following direction. However, in a bioinformatics environment where these individuals are given free reign and are not working as a cohesive unit, you can see why there is so much terrible code and duplication.

Re: A farewell to bioinformatics (2012)

#117

Post by chrisamiller »

kevin_rubyhouse wrote: post #97
chrisamiller wrote: post #62

Some thoughts on this article: - This guy clearly has a limited understanding of the field. This quote is laughable: "There are only two computationally difficult problems in bioinformatics, sequence alignment and phylogenetic tree construction." - As a bioinformatician, I feel sorry for this guy. Just like any other field, there are shitty places to work. If I was stuck in a lab where a demanding PI with no computer…

What's the backstory on the author's tangent about the human genome? It sounded like the human genome project didn't actually do what the name implies.

Tell that to the tens of thousands of researchers who make use of the human reference genome daily. I don't even know what the guy is talking about there - imagining modern genetics or genomics without it is pretty much impossible.

Re: A farewell to bioinformatics (2012)

#118

Post by lemming »

zmmmmm wrote: post #102
lemming wrote: post #46

This is a little discouraging - BioInformatics was my top choice for a Master's program I'm planning to start this year. The program at Melbourne Uni looks really good (accepts from three streams, Math/Stats, Biology or Computing and tailors the course based on your background). Maybe I should go for a more generic Machine Learning one and try to apply that to healthcare in some other field if things are really this…

I'm just starting a PhD at Melbourne Uni in bioinformatics after working in the field for several years. Don't pay any attention to this is my advice. Bioinformatics is a field currently pulling itself up by its own bootstraps out of the realm of research into the clinic. That's a painful process to be sure, but IMHO it's the most profoundly exciting time to be part of any discipline. You are literally being a part o…

Interesting, thanks for the point of view. If you'd be interested in getting a beer or a coffee at any point, let me know - contact details in my profile.

Re: A farewell to bioinformatics (2012)

#119

Post by artmageddon »

michaelhoffman wrote: post #28
hnwh wrote: post #22

Earlier quoted context omitted.

How can you leverage all of us really good programmers with tons of time, who are dying to work on something "important" and meaningful?

If you want to improve the software engineering quality of bioinformatics software, then find an open source project you are interested in, and offer to submit patches to improve really unsexy but important stuff for bioinformatics user experience. Things like documentation, deployment, user interface, and testing. Some of these things require little domain knowledge but no one wants to do them. Edited to add: some p…

Got any examples?

Re: A farewell to bioinformatics (2012)

#120

Post by moondowner »

chrisamiller wrote: post #62

Some thoughts on this article: - This guy clearly has a limited understanding of the field. This quote is laughable: "There are only two computationally difficult problems in bioinformatics, sequence alignment and phylogenetic tree construction." - As a bioinformatician, I feel sorry for this guy. Just like any other field, there are shitty places to work. If I was stuck in a lab where a demanding PI with no computer…

" I could give a rat's ass about performance. I'm trying to find the answer to a question, and if I can get that answer in a reasonable amount of time, then the code is good enough"

This is the only bad point that a lot of people are aligned with.

The more time a program needs to finish, the more time you will need to run it again with some other dataset, and in turn - more time to find the right answer.

I really feel that people with scientific and mathematics background should learn proper programming (not take a course in some language - but have actual experience). Design patterns, data structures, best practices, memory consumption, are all things that should be known before a person starts submitting code for this kind of projects.

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