vcf2maf VS mojo

This is not a part of Bioconductor, but I used this one here: https://github.com/mskcc/vcf2maf

There is a perl script called vcftomaf from here https://github.com/mskcc/vcf2maf

https://github.com/modularml/mojo/blob/main/LICENSE Is this not it?

Interesting, but the intent seems similar to Chris Lattner's new Mojo language which arguably has similar characteristics and is further along in its development.

https://docs.modular.com/mojo/

Mojo is a programming language that combines the performance and control inherent in systems languages like C++ and Rust with the flexibility and simplicity of use typical of dynamic languages like Python. Because of its combination of performance, extensibility, and usability, its design makes it possible to construct high-performance systems, which makes it a good option for AI development.

If you take a look at the optimized Mojo code doing the matrix multiply [1], it takes an expert to understand. It’s not just some simple for-loops in Mojo they’re comparing against.

[1] https://github.com/modularml/mojo/blob/5ce18c47a27c0c4123de1...

Mojo truly unlocks a world of possibilities in high-performance computing.

For a concrete example of Liquid Haskell, see how Gabriella Gonzalez safely removed bound checks of high-performance protocol parsing, in "Scrap your Bounds Checks with Liquid Haskell" [1].

With Liquid Haskell, the bound checks are moved from runtime to compile time, semi-automatically handled by SMT-solvers. With static types, programmers can write correct programs faster, and the programs also run faster.

As an aside, speeding up programs with static analysis (constrained dynamism) are also present in Mojo (a variant of Python) or Swift [2].

[1]: https://github.com/Gabriella439/slides/blob/main/liquidhaske... "Scrap your Bounds Checks with Liquid Haskell"

[2]: https://github.com/modularml/mojo/discussions/466 "Mojo and Dynamism"