Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

A look at the Mojo language for bioinformatics

viralinstruction.com

Post reply on HN
Print view View original

71–80 of 124 posts

Re: A look at the Mojo language for bioinformatics

#71

Post by jakobnissen »

zaptheimpaler wrote: post #60

How does a software engineer transition into bioinformatics or computational biology? I've taken some online courses on bioinformatics and have some experience in large distributed jobs but these jobs seem few and far in between and generally want M.S/PhDs in bioinformatics. Is it really a field that's not viable to enter without an MS?

I'm not sure what's the best strategy to get hired, but professionally, you need to learn as much biology as you can. Cell biology, molecular biology, genetics, physiology. My experience has been that there are a bunch of software engineers in bioinfo already who fall short on the biology side. Differentiate yourself from those.

Re: A look at the Mojo language for bioinformatics

#72

Post by adgjlsfhk1 »

jdiaz97 wrote: post #67
cole-k wrote: post #62

Earlier quoted context omitted.

It's unfortunate indeed if Julia does not have a well-written GC as you imply. While I feel like I have my head wrapped around ownership well enough to write (dare I say idiomatic) Rust without too much difficulty, I do find myself often in a position where I wish I just had a GC. I think this speaks to what your parent comment is saying: I think there are many situations where the performance improvement over having…

I think Julia's GC is quite good now, it can even multithread.

I'd probably describe it as "moderately good". Julia has a pretty major head-start over languages like Java because it can avoid putting most things on the heap in the first place. The main pain point for the GC currently that Julia is missing some escape analysis that would allow it to free mutable objects with short lifetimes (mainly useful for Arrays in loops). The multi-threading definitely helps in a lot of applications though.

Re: A look at the Mojo language for bioinformatics

#73

Post by a_bonobo »

beanjuiceII wrote: post #50
jdiaz97 wrote: post #37

Earlier quoted context omitted.

True, but the title of the blog is about Bioinformatics, and like another comment said: > Bioinformatics is like 0.1% dealing with FASTQ files and the rest is using the ecosystem of libraries for statistics and plotting. Many of them in R Considering that, do you need AOT, memory ownership for doing plotting and statistics? I'd argue not, and that's why R and Python are so popular in Bio.

doesn't this make more sense to have a python like language then for speed? and python for all that other stuff. so learn one'ish language and get it all?

Yeah that's how it ended up for me: large datasets get churned through for speed in Python, but I then usually switch over to R with the summary data because there's just way more biology-specific ecosystem in R than in Python.

R/Bioconductor has packages for human genome-specific analyses so it's easy to download gene positions etc., there are packages for read simulation, amplicon sequence variant detection, gene distance simulations, any kind of RNAseq analysis you can think of... none of these packages exist in Python. If you'd rerun it in Python you'd save 10 minutes or hours of running time but you'd lose days or months re-implementing analyses that are in R packages (plus those R packages often call on C++ code, anyway)

plus ggplot2 is miles ahead of any plotting in Python (to me :) ).

Re: A look at the Mojo language for bioinformatics

#74

Post by jakobnissen »

celrod wrote: post #48
adgjlsfhk1 wrote: post #38

Earlier quoted context omitted.

> I really wish Julia had easy AOT compilation and no GC, that would be perfect I pretty strongly disagree with the no gc part of this. A well written GC has the same throughout (or higher) than reference counting for most applications, and the Rust approach is very cool, but a significant usability cliff for users that are domain first, CS second. A GC is a pretty good compromise for 99% of users since it is a minor…

Too bad Julia doesn't have this theoretical "well written GC". I do not like GCs, so I agree with OP's sentiment. Why solve such a hard problem when you don't have to? I don't find ownership models that difficult. It's things one should be thinking of anyway. I think this provides a good example of where stricter checking/an ownership model like Rust has makes it easier than languages that do not have it (in this cas…

On the other hand, trying to represent graph structures in Rust (e.g. phylogenetic trees, pedigrees, assembly graphs) is absolutely horrible. The ownership models breaks completely apart, and while it can be worked around, it's just a terrible developer experience where I just wished I had a GC in those cases.

Practically speaking I rarely find GC pauses to be an issue, neither latency wise nor speed wise. Though of course that could be due to

1. I don't need low latency in research work,

2. I rarely work with massive complex data structures filling all my RAM where the GC has to scan the whole heap every time it runs, and

3. GC may have indirect performance effects that are not measures as part of GC runs, e.g. by fragmenting active memory more.

Re: A look at the Mojo language for bioinformatics

#75

Post by eyegor »

a_bonobo wrote: post #73
beanjuiceII wrote: post #50

Earlier quoted context omitted.

doesn't this make more sense to have a python like language then for speed? and python for all that other stuff. so learn one'ish language and get it all?

Yeah that's how it ended up for me: large datasets get churned through for speed in Python, but I then usually switch over to R with the summary data because there's just way more biology-specific ecosystem in R than in Python. R/Bioconductor has packages for human genome-specific analyses so it's easy to download gene positions etc., there are packages for read simulation, amplicon sequence variant detection, gene d…

To your last point, have you tried plotnine? It's meant to be ggplot2 for python.

https://github.com/has2k1/plotnine

Re: A look at the Mojo language for bioinformatics

#76

Post by gandalfgeek »

> It does grate me then, when someone else manages to raise 100M dollars on the premise of reinventing the wheel to solve the exact same problem, but from a worse starting point because they start from zero and they want to retain Python compatibility. Think of what money like that could do to Julia!

Python is a juggernaut with total control of the ML space and is a huge part (even if less dominant) in modern scientific computing.

A VC has way better chances of success building solutions compatible with Python rather than replacing it.

Re: A look at the Mojo language for bioinformatics

#77

Post by eyegor »

alpaca128 wrote: post #21
tehsauce wrote: post #11

Earlier quoted context omitted.

How much does this particular result change when running in release mode?

Depending on the code I've seen performance increases above 100x in some cases. While that's not exactly the norm, benchmarking Rust in debug mode is absolutely pointless even as a rough estimate.

Is there any compiled language that doesn't benefit heavily from release builds? That would be interesting if true.

Re: A look at the Mojo language for bioinformatics

#78

Post by MillironX »

folli wrote: post #45
f6v wrote: post #3

As someone who practices bioinformatics, it doesn’t seem appealing. Bioinformatics is like 0.1% dealing with FASTQ files and the rest is using the ecosystem of libraries for statistics and plotting. Many of them in R, by the way.

I guess that depends on your exact ecological niche within bioinformatics. I got my start at a NGS facility, so handling FASTQ was closer to 80% of my time, so any speedups would have been greatly appreciated.

> I guess that depends on your exact ecological niche within bioinformatics.

Agreed. I know people in my department who just ran Galaxy pipelines and R scripts to make pretty plots. I was on the other side of the spectrum and needed fast parsers, so the SAM and VCF specifications were my bible.

Re: A look at the Mojo language for bioinformatics

#79

Post by getoffmycase »

__MatrixMan__ wrote: post #19
f6v wrote: post #3

As someone who practices bioinformatics, it doesn’t seem appealing. Bioinformatics is like 0.1% dealing with FASTQ files and the rest is using the ecosystem of libraries for statistics and plotting. Many of them in R, by the way.

As someone who is considering a switch from generic software engineering towards bioinformatics, what would you say the pain points are? If this is not the way to remove workflow friction, what is?

Honestly the major pain point is that the grad student that wrote the package you need is no longer maintaining it because they’ve graduated. Also the code they wrote sucks, but whatever.

I’m wary of software engineers coming over the bioinformatics because they never have the domain expertise required to make meaningful contributions, and yet many think they know everything.

Re: A look at the Mojo language for bioinformatics

#80

Post by adgjlsfhk1 »

eyegor wrote: post #77
alpaca128 wrote: post #21

Earlier quoted context omitted.

Depending on the code I've seen performance increases above 100x in some cases. While that's not exactly the norm, benchmarking Rust in debug mode is absolutely pointless even as a rough estimate.

Is there any compiled language that doesn't benefit heavily from release builds? That would be interesting if true.

Julia, for example runs by default with -O2 and debug info turned on. It's a good combo between debug-ability and performance.
Post reply on HN