An updated phylogeny of bats is presented, based on new genome assemblies and many ancient fossils and including all known bat families.

I started learning about bioinformatics back in the days when a lot of bioinformatics was done in Perl. Nowadays I don't see much Perl used in new tools, which are mostly written in Python or R or both. Perl was dominant back in the day because you could get work done _quickly_. Perl is a forgiving language and very flexible but both of these become a problem if you don't have good fundamentals and I believe this is the reason why Perl became so hated in bioinformatics. A lot of bioinformatics practitioners came from a biology background, like myself, and had never studied programming and/or computer science. But if you persevered and just stubbornly go into a cycle of editing and re-running, you can come up with a "functioning" Perl script, which I mean one that simply doesn't throw an error. You wouldn't use this script blindly of course and would _see_ if the output matches your expectations. If it does, then that becomes the Perl script for that particular task. Fast forward weeks or months later when you need to reuse that script on new data, it doesn't work anymore because _something_ has changed. Then you go into this cycle of editing and re-running again to get it working for the new data. This generates a script that ultimately works but has included conditionals, hardcoded options, and whatnot that needed to be included to accommodate the new data and the old. This script gets shared come publication time and "custom Perl script" is used in the methods description, adding another data point for Perl and irreproducible work or at best, work that is difficult to reproduce. I know this cycle because I practised it and had I known better, I wouldn't have done it; and that's the point: I didn't know. Now, there's nothing wrong with Perl and perhaps it doesn't deserve the bad rep it gets. I managed to get a lot of work done and could create useful web applications using Perl. But I really think it's a language meant for someone like the seasoned system admin who seems to know everything and has limited time because they need to manage too many things at once. They can leverage the flexibility and terseness of Perl to really create something that keeps systems together. Furthermore, in a lot of labs there's usually only one sys admin, so only they need to understand how all the scripts works. But for someone without the computing background, it can be disastrous, which was what happened with Perl and bioinformatics. I have found that the most useful thing to do when somebody doesn't know what they are doing, is to _limit_ what they can do. And then this comes to the same point where you need to choose between how limiting something is, which is safer, versus flexibility, which makes it more usable. I can't remember when the realisation hit me that Perl and bioinformatics back in the day is just like AI and bioinformatics right now. I wanted to write about it then but have put it off until now. Once again, we have a tool (AI) that helps us get work done _quickly_ and this time almost effortlessly. You simply describe what you want to get done in a prompt and all the code and documentation (if you also included this) is outputted. You may argue that Perl and AI are two completely different tools, and they are, but to me, the issue is fundamentally the same: you have somebody _without_ the background knowledge trying to create something that _requires_ the background knowledge. Like Perl, you can use AI to speed things up but _only_ if you are that seasoned sys admin. Lior Pachter implemented edgeR in Python in a week by using Claude; from the pre-print: > This project was completed in one week with Claude Opus 4.5 and Opus 4.6 performing the coding. As someone who had not coded seriously for more than 20 years, I found Claude’s efficiency and accuracy enabling in a way that would have been inconceivable a few years ago. At this point, it is reasonable to predict that what took a week for this project may soon take less than a day. This raises the question of what the role of preprints, conference proceedings, and journal publications will be in the scientific enterprise of the future (Zeilberger, 1993) That's a very substantial statement made even more substantial because of who said it. But I don't think I could have vibe coded a technically correct version of edgeR in Python. I simply don't have the deep statistical and computational background to verify the output. I firmly believe that you need the solid fundamentals and foundations to be able to use AI effectively to build what you want. With the current state of the models and my experience with them, I don't think all of that can be outsourced to AI (and even if you could, you shouldn't). But perhaps I'm prompting it wrong and/or not using AI properly. To end, I wonder if the modern day equivalent of "custom Perl script" will be "coded by AI" in bioinformatics. I wonder if a marker for poor quality and/or irreproducible work will be any mention of AI coding in the methods. It's not that AI (or Perl) does poor work, it's just that they make it too _easy_ to generate plausible output and when AI is used without the necessary background knowledge, it will produce sub-optimal work. This work is licensed under a Creative Commons Attribution 4.0 International License. ### Like this: Like Loading… ### _Related_

The Foundational Questions Institute, FQxI, catalyzes, supports, and disseminates research on questions at the foundations of science, particularly new frontiers and innovative ideas integral to a deep understanding of reality, but unlikely to be supported by conventional funding sources.

Nearly one-quarter of heatwaves would have been ‘virtually impossible’ without global warming — and can be attributed to the emissions of individual energy producers.

<a href='https://fed.brid.gy/'>Bridgy Fed</a> is a bridge between decentralized social networks like the <a href='https://indieweb.org/'>IndieWeb</a>, <a href='https://en.wikipedia.org/wiki/Fediverse'>fediverse</a>, <a href='https://bsky.social/'>Bluesky</a>, and others. <a href='https://fed.brid.gy/docs'>More info here.</a>

Adaptive radiations often occur with an early burst of ecological diversification, which requires not only various available niches but also a generalist ancestor with wide ecological niche breadths. However, ancestral generalism remains hard to test in empirical cases. The New World leaf-nosed bats (family Phyllostomidae) represent an unparalleled mammalian adaptive radiation with diverse dietary niches including arthropods, blood, terrestrial vertebrates, nectar, and fruits. However, when and how often phyllostomid bats transitioned from insectivory to fruit or nectar feeding remains unclear. Here we tested the hypotheses of ancestral insectivory versus ancestral omnivory in Phyllostomidae (141 species) using improved trait reconstructions based on multi-response phylogenetic threshold models, while explicitly accounting for phylogenetic uncertainty. Our results indicate that complementary fruit feeding has fully evolved at the early burst of the phyllostomid radiation and started to evolve in the most recent common ancestor of the family, supporting the ancestral omnivory hypothesis. In addition, fruit feeding probably evolved before nectar eating in Phyllostomidae, in contrast to the claims of previous studies. Extending this analysis to all bat families (621 species) reveals independent evolution of ancestral fruit feeding in four families, namely the Pteropodidae (Old World fruit bats) and three families from the Noctilionoidea superfamily. Despite the ancestral omnivory of these fruit-eating families, only Phyllostomidae and Pteropodidae show high species diversity and evolved predominant and strict fruit feeding. Therefore, our results reveal that ancestral generalism (i.e., omnivory) may be a precondition of but does not necessarily lead to adaptive radiations which also require subsequent niche partitioning and speciation. ### Competing Interest Statement The authors have declared no competing interest.

A systematic analysis of 115 mammalian genomes, including 10 new bat genomes, reveals prevalent positive selection in immune genes in bats and shows key adaptations in the antiviral gene ISG15 that aid disease resistance in bats, including to coronaviruses.

OR: Rickets, please!