Trainable Probability Inference Engine on RNA Structural Alignment

Installation

This project is written in Rust, a systems programming language. You need to install Rust components, i.e., rustc (the Rust compiler), cargo (the Rust package manager), and the Rust standard library. Visit the Rust website to see more about Rust. You can install Rust components with the following one line:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Rustup arranges the above installation and enables to switch a compiler in use easily. You can install ConsProb:

# AVX, SSE, and MMX enabled for rustc
# Another example: RUSTFLAGS='--emit asm -C target-feature=+avx2 -C target-feature=+ssse3 -C target-feature=+mmx -C target-feature=+fma'
RUSTFLAGS='--emit asm -C target-feature=+avx -C target-feature=+ssse3 -C target-feature=+mmx' \
  cargo install consprob-trained

Check if you have installed ConsProb properly:

# Its available command options will be displayed
consprob_trained

You can run ConsProb with a prepared test set of sampled tRNAs:

git clone https://github.com/heartsh/consprob-trained \
  && cd consprob-trained
cargo test --release
# The below command requires Gnuplot (http://www.gnuplot.info)
# Benchmark results will be found at "./target/criterion/report/index.html"
cargo bench

Docker Playground

I offer my Docker-based playground for RNA software and its instruction to replay my computational experiments easily.

Method Digest

ConsProb-Turner can compute a variety of sparse posterior probabilities on RNA pairwise structural alignment using Turner's model. This repository offers ConsProb-trained, a machine-learning counterpart of ConsProb-Turner. This repository also includes a ConsTrain, structural alignment-based machine-learning method for ConsProb-trained.

Author

Heartsh

License

Copyright (c) 2018 Heartsh
Licensed under the MIT license.