Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch

Related tags

Deep Learning machine-learning bioinformatics pytorch biological-sequences hierarchical-clustering multiple-sequence-alignment
Overview

Neural Distance Embeddings for Biological Sequences

Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch. NeuroSEED is a novel framework to embed biological sequences in geometric vector spaces. Preprint will we published soon.

diagram

Overview

The repository is organised in four main folders one for each of the tasks analysed. Each of these contain scripts and models used for the task as well as instructions on how to run them and the tuned hyperparameters found.

  • edit_distance for the edit distance approximation task
  • closest_string for the closest string retrieval task
  • hierarchical_clustering for the hierarchical clustering task, further divided in relaxed and unsupervised for the two approaches explored
  • multiple_alignment for the multiple sequence alignment task, further divided in guide_tree and steiner_string
  • util contains a series of utility routines shared between all the tasks
  • tests contains a wide range of tests for the various components of the repository

Installation

Create a virtual (or conda) environment and install the dependencies:

python3 -m venv neuroseed
source neuroseed/bin/activate
pip install -r requirements.txt

Then install the mst and unionfind packages used for the hierarchical clustering:

cd hierarchical_clustering/relaxed/mst; python setup.py build_ext --inplace; cd ../../..
cd hierarchical_clustering/relaxed/unionfind; python setup.py build_ext --inplace; cd ../../..

License

MIT

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Comments
  • Do you only focus on unsupervised sequence similarity/distance? How to consider functional similarity/distance?

    Do you only focus on unsupervised sequence similarity/distance? How to consider functional similarity/distance?

    Dear authors,

    Greetings!

    I would like to check do you only focus on unsupervised sequence similarities/distances of biological sequences? If so, do you have a plan to consider their functional similarities/distances?

    Thanks. (Amos) Xinshao Wang

    opened by xinshao-wang 2
  • Greengenes dataset link is broken

    Greengenes dataset link is broken

    Hi all, It seems that the download link for the Greengenes dataset is no longer present in the google drive folder. It's not a huge deal since I can just try to get it directly from the source, but I thought I'd let you know. On that note, is there any sort of preprocessing/QC done on the sequences in the dataset that I should be aware of? I'm interested in using this data to reproduce some results.

    opened by mchowdh200 0
  • What versioon of geomstats are you using?

    What versioon of geomstats are you using?

    What version of geomstats are you using? I keep getting errors with it. Different error dependent on the version. I tried version 2.4.2, 2.2.2, and 2.3.1.

    opened by nongiga 1
Owner
Gabriele Corso
PhD student @ MIT • Research on Graph and Geometric Representation Learning • Previously intern @ Twitter Research, D.E. Shaw and IBM
Gabriele Corso
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