| Rust 1.82.0 just got released (https://blog.rust-lang.org/2024/10/17/Rust-1.82.0.html), bringing some really good updates such as: - The new cargo info subcommand
- aarch64-apple-darwin is now a tier 1 target
- Improvements to impl Trait syntax This raises an interesting discussion about the programming languages used in computational biology. While Python and R are predominant, there may be other good alternatives worth considering, for example - 1. Crystal: Known for its fast compile times and ease of use, Crystal is reportedly efficient in handling large biological data files. Has anyone used it in production? Heng Li's benchmarks provide some insights: https://lh3.github.io/2020/05/17/fast-high-level-programming-languages. 2. Rust: There is robustness in context of software development, but how effective is it for everyday computational biology scripting? Heng Li offers another perspective: https://lh3.github.io/2024/03/05/what-high-performance-language-to-learn. 3. Julia: Gaining popularity, but frankly I have concerns regarding numerical precision. (Maybe I am wrong), but tooling and non-compiled language makes it not okay for me personally. 4. Zig: I do not know that it is even relevant in this topic - but I wanted to add to capture a possibility? What programming languages do you prefer for tasks such as sequence analysis, modeling, or data processing? Are there any lesser-known languages that deserve more attention, such as Racket or Raku? Could you please share your experiences, whether in serious production environments or personal hobby projects. I would love to hear about different languages, paradigms, and their applications. Additionally, I am exploring new ideas for a SAM/BAM encoding scheme for biological data. If anyone is interested in collaborating on this project, please reach out. |