gww

A few of the older 3d MMORPGS did try to instill some of those mechanics. Examples include:

1) Asheron's Call (which had a very active mod scene and now an emulator scene). I don' think the servers are nearly as popular as UO though.

2) Shadowbane (this one was heavily guild based but it was fun being a bit of an outlaw and PvPing random people and guilds).

I have worked at a sequencing center before. DNA contamination is easier to mitigate because the lab disposables aren't made out of what you are testing. Disposables are almost always plastic and tend to have minimal DNA contamination. Environmental DNA contamination is largely mitigated with PCR hoods and careful protocols/practices. However, these procedures don't mitigate DNA contamination at the collection level, which is likely where the statistical models you mentioned help.

I can't imagine wafting your hands over the tubes would change the plastic amounts substantially compared to whatever negative controls the papers used. But again, I am not an expert on this kind of analytical chemistry. I always worry more about batch effects. But it does seem like microplastics are becoming the new microbiome.

I haven't really read the studies but shouldn't they have negative controls to negate these effects? Wouldn't that let the author's correct for a baseline contamination level in the lab?

That is how I became serious about programming. I played around a bit but I never really wrote anything useful until I started playing Asheron's Call. I learned C++ to write bots and other plugins for Decal (an embedded mod framework).

There are other examples of oncoviruses including: Epstein-Barr virus and Human herpes virus 8 (Kaposi's Sarcoma).

Makes me wonder: Is the cancer "industry" searching for causes or just after-the-fact treatment?

There are billions of dollars spent on this problem through huge DNA/RNA/Epigenomics/Chromatin Accessibility sequencing initiatives. There is also a huge amount of model system work such as mouse models.

ed was the hardest thing for 12 year old me to understand.

I played Aardwolf briefly but I mostly stuck to a nightmare/LP MUD that is still around but essentially empty. I learned to code (which ended up being a gateway to C/C++), grief, and be compassionate to new players.

There's a second paper on the same topic published in Nature [1]

[1] https://www.nature.com/articles/s41586-025-09771-9

I have a different problem with my M3 Macbook Pro. If I leave chrome (sometimes other apps too) open with the macbook plugged in and the lid closed the computer will get very warm and stay very warm until I unplug it / close chrome.

Edit: It's also not warm when plugged in and using chrome with the lid open.

Reminds me of one of my favorite Simpsons lines: "Aw, you can come up with statistics to prove anything, Kent. Forty percent of all people know that"

They are used somewhat commonly in bioinformatics where lookup tables can have long keys and many entries [1].

1. https://en.m.wikipedia.org/wiki/Bloom_filters_in_bioinformat...

This is a great analogy. One small change is that there are two ways to reassemble it. One is to try to blindly put the pieces together and fork a teacup (read assembly) vs trying to use a picture of the teacup to figure out where the pieces go (read alignment / mapping)

They didn't sequence the whole human genome (~3 billion bases) for multiple reasons. I am not an expert on ancient DNA but I will try to explain the paper as best I can:

1. Contamination with other flora and fauna DNA 2. Relative low proportions of human DNA 3. The DNA is usually highly degraded, which limits the analyses to short read sequencing (in this case they used 76 bp reads). The halflife of human DNA is ~521 years.

To mitigate these problems they used multiple targeted approaches including one to isolate mitochondrial DNA, where they managed to sequence the whole ~16kb human mtDNA, where each base was covered by 62 sequencing reads on average (62x coverage).

They used another to isolate specific regions containing single nucleotide polymorphisms (SNPs), which are DNA mismatches known to be related to ancient human DNA and humans. They targeted 470,724 single nucleotide polymorphisms of which 70% (336,429) were recovered.

They did perform shotgun sequencing on all of the DNA isolated, but due to species assignment issues they again focused on fragments that contain diagnostic SNPs in these cases they only recovered a small number of SNPs per sample, again due to the relatively low proportion of human DNA and its degradation (20,526, 3,734, 124,862, 85,901, 34,756, 41,632, 34,677 and 72,992) as per the legend in figure 3.

The BWT based FM-index is one of my favorite data structures. It's used frequently for DNA mapping, where the 4 letter alphabet can be encoded in two bits and the occurrence function can use clever caching, bit bashing and the pop count function to get nice performance.

Users of these kinds of tools should check that their marker genes are associated with the labelled cell types. There are known markers for many cell types across multiple organisms.

Will check it out. Thanks a lot

Cool thanks

Could this also be adapted for gene set enrichment? For example, if I had a set(s) of genes from an ATAC-seq experiment would it be able to guess their function / cell types?

This is really useful thanks for sharing. My students and myself tend to waste a lot of time annotating clusters and have not found a reasonable solution yet. This will be fun to try.

It is common for human cancers to be polyploid after accumulate whole genome doublings (WGD), where a tumour cells goes from being approximately diploid to tetraploid. Different tumour types have higher rates of WGD, for example, glioblastoma, ovarian cancer, and pancreatic adenocarcinoma. But what usually happens is that the tumour loses parts of the doubled genome to reach a ploidy (average copy number across the genome) of 3-4ish.

I am going to steal that name. I find that computational overcooking happens more and more because the easy questions that can be asked with sequencing datasets is starting to dry up.

I would argue that these are some of the most important algorithms in bioinformatics/biology. They have a wide range of applications.

Grails test isn't for somatic mutations but cancer specific DNA methylation patterns. DNA methylation is slightly more challenging to work with due to how the methylation is identified from sequencing data. There's a chemical of enzymatic step to convert unmethylated bases to another base. In humans it's usually unmethylated cytosines to thymines. The analysis is also more complicated but mostly standard now. Nonetheless, DNA methylation has proven to be very useful for liquid biopsies.

Beautiful book I used it in undergrad. The only problem with the book is its size; it's terrible to lug around in a backpack.

You are a bit behind on the available read lengths. The Oxford nanopore can produce reads as long as 300kb (k as in kilo) and the PacBio hifi can product 15 to 20kb reads. Finally a now defunct technology by 10x genomics called linked read sequencing also permitted longer regions to be sequences using short reads.

I am a bioinformatics scientist that works almost entirely remotely. I miss the impromptu discussions that happen around the lab, which can help solve a problem you or a co-worker is stuck on or they can lead to a new and interesting collaborations. I also miss most of the invited speaker series because they stopped providing a virtual option, which is very frustrating.

Edit: I don't miss the noise of young trainees treating the office space like a party room, the desks with poor ergonomics for programming work, or the annoying commute. So there are definitely advantages to counteract the loneliness.

Sounds like a way for HowToBasic to generate new YouTube video ideas.

My first real coding education was Pascal on a VAX/VMS terminal in high school. It was a great experience and I miss the monochrome orange screens

The length limits also forces authors to increase the complexity of their figures to cram as much data into the limited space as possible. I've seen papers with figures that have 10+ graphs all squished into a half page figure.

EDIT: This usually limits proper explanations of the data in the text too because of very limited word counts.

I wonder if that would be cheaper in the long term than the typical NFS plus off site tape backups and/or cloud backup used by the places I have worked. I suspect it would become a logistical nightmare to store and keep track of thousands of SD cards.