To be a next-generation DL-based phenotype prediction from genome mutations.

Overview
Sequence -----------+--> 3D_structure --> 3D_module --+                                      +--> ?
|                   |                                 |                                      +--> ?
|                   |                                 +--> Joint_module --> Hierarchical_CLF +--> ?
|                   |                                 |                                      +--> ?
+-> NLP_embeddings -+-------> Embedding_module -------+                                      +--> ?

ClynMut: Predicting the Clynical Relevance of Genome Mutations (wip)

To be a next-generation DL-based phenotype prediction from genome mutations. Will use sota NLP and structural techniques.

Planned modules will likely be:

  • 3D learning module
  • NLP embeddings
  • Joint module + Hierarchical classification

The main idea is for the model to learn the prediction in an end-to-end fashion.

Install

$ pip install clynmut

Example Usage:

import torch
from clynmut import *

hier_graph = {"class": "all", 
              "children": [
                {"class": "effect_1", "children": [
                  {"class": "effect_12", "children": []},
                  {"class": "effect_13", "children": []}
                ]},
                {"class": "effect_2", "children": []},
                {"class": "effect_3", "children": []},
              ]}

model = MutPredict(
    seq_embedd_dim = 512,
    struct_embedd_dim = 256, 
    seq_reason_dim = 512, 
    struct_reason_dim = 256,
    hier_graph = hier_graph,
    dropout = 0.0,
    use_msa = False,
    device = None)

seqs = ["AFTQRWHDLKEIMNIDALTWER",
        "GHITSMNWILWVYGFLE"]

pred_dicts = model(seqs, pred_format="dict")

Important topics:

3D structure learning

There are a couple architectures that can be used here. I've been working on 2 of them, which are likely to be used here:

Hierarchical classification

  • A simple custom helper class has been developed for it.

Testing

$ python setup.py test

Datasets:

This package will use the awesome work by Jonathan King at this repository.

To install

$ pip install git+https://github.com/jonathanking/sidechainnet.git

Or

$ git clone https://github.com/jonathanking/sidechainnet.git
$ cd sidechainnet && pip install -e .

Citations:

@article{pejaver_urresti_lugo-martinez_pagel_lin_nam_mort_cooper_sebat_iakoucheva et al._2020,
    title={Inferring the molecular and phenotypic impact of amino acid variants with MutPred2},
    volume={11},
    DOI={10.1038/s41467-020-19669-x},
    number={1},
    journal={Nature Communications},
    author={Pejaver, Vikas and Urresti, Jorge and Lugo-Martinez, Jose and Pagel, Kymberleigh A. and Lin, Guan Ning and Nam, Hyun-Jun and Mort, Matthew and Cooper, David N. and Sebat, Jonathan and Iakoucheva, Lilia M. et al.},
    year={2020}
@article{rehmat_farooq_kumar_ul hussain_naveed_2020, 
    title={Predicting the pathogenicity of protein coding mutations using Natural Language Processing},
    DOI={10.1109/embc44109.2020.9175781},
    journal={2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
    author={Rehmat, Naeem and Farooq, Hammad and Kumar, Sanjay and ul Hussain, Sibt and Naveed, Hammad},
    year={2020}
@article{pagel_antaki_lian_mort_cooper_sebat_iakoucheva_mooney_radivojac_2019,
    title={Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome},
    volume={15},
    DOI={10.1371/journal.pcbi.1007112},
    number={6},
    journal={PLOS Computational Biology},
    author={Pagel, Kymberleigh A. and Antaki, Danny and Lian, AoJie and Mort, Matthew and Cooper, David N. and Sebat, Jonathan and Iakoucheva, Lilia M. and Mooney, Sean D. and Radivojac, Predrag},
    year={2019},
    pages={e1007112}
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Comments
  • TO DO LIST

    TO DO LIST

    • [x] Add embeddings functionality
    • [ ] Add 3d structure module (likely-to-be GVP/... based)
    • [x] Add classifier
    • [x] Hierarchical classification helper based on differentiability
    • [x] End-to-end code
    • [ ] data collection
    • [ ] data formatting
    • [ ] Run featurization for all data points (esm1b + af2 structs)
    • [ ] Perform a sample training
    • [ ] Perform sample evaluation
    • [ ] Iterate - improve
    • [ ] ...
    • [ ] idk, will see as we go
    opened by hypnopump 0
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