Git Product home page Git Product logo

linearturbofold-1's Introduction

LinearTurboFold

This repository contains the C++ source code for the LinearTurboFold project, an end-to-end linear-time algorithm for structural alignment and conserved structure prediction of RNA homologs, which is the first joint-fold-and-align algorithm to scale to full-length SARS-CoV-2 genomes without imposing any constraints on base-pairing distance.

LinearTurboFold: Fast Folding and Alignment for RNA Homologs with Applications to Coronavirus

Sizhen Li, He Zhang, Liang Zhang, Kaibo Liu, Boxiang Liu, David Mathews*, Liang Huang*

* corresponding authors

Dependency

gcc 4.8.5 or above;
python2.7

Compile

Make

Run

LinearTurboFold can be run with:

./linearturbofold -i input.fasta -o output_dir [OPTIONS]

The input file should be in the FASTA format. Please see input.fasta as an example.
Output a multiple sequence alignment and predicted secondary structures in the output directory.

OPTIONS

--it The number of iterations (default 3).
--b1 The beam size for LinearAlignment (default 100, set 0 for infinite beam).
--b2 The beam size for LinearPartition (default 100, set 0 for infinite beam).
--pf Save partition functions for all the sequencs after the last iteration (default False).
--bpp Save base pair probabilities for all the sequencs after the last iteration (default False).
-v Print out alignment, folding and runtime information (default False).
--th Set ThreshKnot threshknot (default 0.3).
--tkit Set ThreshKnot iterations (default 1).
--tkhl Set ThreshKnot minimum helix length (default 3).

Example

./linearturbofold -i input.fasta -o results/ --pf --bpp
100% [==================================================]
3 iterations Done!
Outputing partition functions to files ...
Outputing base pair probabilities to files ...
Outputing multiple sequence alignment to results/output.aln...
Outputing structures to files ...

Evalutation Dataset

We used the RNAStralign dataset with known alignments and structures to evaluate LinearTurboFold and benchmarks.

SARS-CoV-2 Dataset and Results

The 25 SARS-CoV-2 and SARS-related genomes analyzed in the paper are listed in samples25.fasta.
For further study by experts, we provide the whole multiple sequence alignment and predicted structures for all genomes from LinearTurboFold in sars-cov-2_and_sars-related_25_genomes_msa_structures.txt.
Each genome corresponds to three lines: sequence name, aligned sequence and aligned structure, respectively.

linearturbofold-1's People

Contributors

sizhen avatar linearfold avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.