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

Eric Alcaide

Last update: Jan 11, 2022

Related tags

Text Data & NLP ClynMut

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:

GVP
E(n)-gnn

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

$ 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
[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

Releases(0.0.2)

0.0.2(Mar 16, 2021)
Adds main architecture

Adds main helper functions

After some tests and fixes, and trials, it should be ready.

Source code(tar.gz)
Source code(zip)
0.0.01(Mar 12, 2021)

Initial infra. Statement of purpose and wip
Source code(tar.gz)
Source code(zip)

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

Related tags

Overview

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

Install

Example Usage:

Important topics:

3D structure learning

Hierarchical classification

Testing

Datasets:

Citations:

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Eric Alcaide

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