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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