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draupnir-0.0.27
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مشاهده بیشترتوضیحات
Ancestral sequence reconstruction using a tree structured Ornstein Uhlenbeck variational autoencoder
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | draupnir-0.0.27 |
| نام | draupnir |
| نسخه کتابخانه | 0.0.27 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lys Sanz Moreta |
| ایمیل نویسنده | lys.sanz.moreta@outlook.com |
| آدرس صفحه اصلی | https://github.com/LysSanzMoreta/DRAUPNIR_ASR |
| آدرس اینترنتی | https://pypi.org/project/draupnir/ |
| مجوز | - |
DRAUPNIR: "Beta library version for performing ASR using a tree-structured Variational Autoencoder"
<p align="center">
<img src="https://github.com/LysSanzMoreta/DRAUPNIR_ASR/blob/main/draupnir/src/draupnir/images/draupnir_logo.png" height="auto" width="790" style="border-radius:50%">
</p>
**Extra requirements for tree inference:**
#These are NOT necessary if you have your own tree file or for using the default datasets
IQ-Tree: http://www.iqtree.org/doc/Quickstart
```
conda install -c bioconda iqtree
```
RapidNJ: https://birc.au.dk/software/rapidnj
```
conda config --add channels bioconda
conda install rapidnj
```
**Extra requirements for fast patristic matrix construction**
#Recommended if you have more than 200 sequences. The patristic matrix is constructed only once
Install R (R version 4.1.2 (2021-11-01) -- "Bird Hippie"
)
```
sudo apt update & sudo apt upgrade
sudo apt -y install r-base
```
together with ape 5.5 and TreeDist 2.3 libraries
```
install.packages(c("ape","TreeDist"))
```
**Draupnir pip install**
```
pip install draupnir
```
**Example**
```
See Draupnir_example.py
```
**Which guide to use?**
By experience, use delta_map, the marginal results (Test folder) are the most stable.
It is recommended to run the model both with the variational and the delta_map guides and compare outputs using the mutual information.
If necessary run the variational guide longer than the delta_map, since it has more parameters to infere and takes longer.
**How long should I run my model?**
0) Before training:
- It is recommended to train for at least 10000 epochs in datasets <800 leaves. See article for inspiration, the runtimes where extended to achieve maximum benchmarking accuracy, but it should not be necessary.
1) While it is training:
- Check for the Percent_ID.png plot, if the training accuracy has peaked to almost 100%, run for at least ~1000 epochs more to guarantee full learning
- Check for stabilization of the error loss: ELBO_error.png
- Check for stabilization of the entropy: Entropy_convergence.png
2) After training:
- Observe the latent space:
1) t_SNE, UMAP and PCA plots: Is it organized by clades? Although, not every data set will present tight clustering of the tree clades though but there should be some organization
<p align="center">
<img src="https://github.com/LysSanzMoreta/DRAUPNIR_ASR/blob/main/draupnir/src/draupnir/images/LatentBlactamase.png" alt="Latent space" width="300" />
</p>
2) Distances_GP_VAE_z_vs_branch_lengths_Pairwise_distance_INTERNAL_and_LEAVES plot: Is there a positive correlation? If there is not a good correlation but the train percent identity is high, it will still be a valid run
- Observe the sampled training (leaves) sequences and test (internal) sequences: Navigate to the Train_argmax and Test_argmax folders and look for the .fasta files
- Calculate mutual information:
- First: Run Draupnir with the MAP & Marginal version and Variational version, or just the Variational
- Second: Use the draupnir.calculate_mutual_information() with the paths to the folders with the trained runs.
**Datasets**
#They are recommended to use with the pipeline, look into datasets.py for more details
```
dict_urls = {
"aminopeptidase":"https://drive.google.com/drive/folders/1fLsOJbD1hczX15NW0clCgL6Yf4mnx_yl?usp=sharing",
"benchmark_randall_original_naming":"https://drive.google.com/drive/folders/1oE5-22lqcobZMIguatOU_Ki3N2Fl9b4e?usp=sharing",
"Coral_all":"https://drive.google.com/drive/folders/1IbfiM2ww5PDcDSpTjrWklRnugP8RdUTu?usp=sharing",
"Coral_Faviina":"https://drive.google.com/drive/folders/1Ehn5xNNYHRu1iaf7vS66sbAESB-dPJRx?usp=sharing",
"PDB_files_Draupnir_PF00018_116":"https://drive.google.com/drive/folders/1YJDS_oHHq-5qh2qszwk-CucaYWa9YDOD?usp=sharing",
"PDB_files_Draupnir_PF00400_185": "https://drive.google.com/drive/folders/1LTOt-dhksW1ZsBjb2uzi2NB_333hLeu2?usp=sharing",
"PF00096":"https://drive.google.com/drive/folders/103itCfxiH8jIjKYY9Cvy7pRGyDl9cnej?usp=sharing",
"PF00400":"https://drive.google.com/drive/folders/1Ql10yTItcdX93Xpz3Oh-sl9Md6pyJSZ3?usp=sharing",
"SH3_pf00018_larger_than_30aa":"https://drive.google.com/drive/folders/1Mww3uvF_WonpMXhESBl9Jjes6vAKPj5f?usp=sharing",
"simulations_blactamase_1":"https://drive.google.com/drive/folders/1ecHyqnimdnsbeoIh54g2Wi6NdGE8tjP4?usp=sharing",
"simulations_calcitonin_1":"https://drive.google.com/drive/folders/1jJ5RCfLnJyAq0ApGIPrXROErcJK3COvK?usp=sharing",
"simulations_insulin_2":"https://drive.google.com/drive/folders/1xB03AF_DYv0EBTwzUD3pj03zBcQDDC67?usp=sharing",
"simulations_PIGBOS_1":"https://drive.google.com/drive/folders/1KTzfINBVo0MqztlHaiJFoNDt5gGsc0dK?usp=sharing",
"simulations_sirtuins_1":"https://drive.google.com/drive/folders/1llT_HvcuJQps0e0RhlfsI1OLq251_s5S?usp=sharing",
"simulations_src_sh3_1":"https://drive.google.com/drive/folders/1tZOn7PrCjprPYmyjqREbW9PFTsPb29YZ?usp=sharing",
"simulations_src_sh3_2":"https://drive.google.com/drive/folders/1ji4wyUU4aZQTaha-Uha1GBaYruVJWgdh?usp=sharing",
"simulations_src_sh3_3":"https://drive.google.com/drive/folders/13xLOqW2ldRNm8OeU-bnp9DPEqU1d31Wy?usp=sharing"
}
```
| Dataset | Number of leaves | Alignment lenght | Name |
|:-------------------------------------------------:|:----------------:|:----------------:|-----------------------------------|
| Randall's Coral fluorescent proteins (CFP) | 19 | 225 | benchmark_randall_original_naming |
| Coral fluorescent proteins (CFP) Faviina subclade | 35 | 361 | Coral_Faviina |
| Coral fluorescent proteins (CFP) subclade | 71 | 272 | Coral_all |
| Simulation $\beta$-Lactamase | 32 | 314 | simulations_blactamase_1 |
| Simulation Calcitonin | 50 | 71 | simulations_calcitonin_1 |
| Simulation SRC-Kinase SH3 domain | 100 | 63 | simulations_src_sh3_1 |
| Simulation Sirtuin | 150 | 477 | simulations_sirtuins_1 |
| Simulation SRC-kinase SH3 domain | 200 | 128 | simulations_src_sh3_3 |
| Simulation PIGBOS | 300 | 77 | simulations_PIGBOS_1 |
| Simulation Insulin | 400 | 558 | simulations_insulin_2 |
| Simulation SRC-kinase SH3 domain | 800 | 99 | simulations_src_sh3_2 |
**What do the folders mean?**
1) If you selected **delta_map** guide:
1) Train_Plots: Contains information related to the inference of the train sequences (the leaves). They are samples obtained by using the MAP estimates of the logits.
2) Train_argmax_Plots: Single sequence per leaf obtained by the using the most likely amino acids indicated by the logits ("argmax the logits")
3) Test_Plots: Samples for the test sequences (ancestors). In this case they contain the sequences sampled using the marginal probability approach (equation 5 in the paper)
4) Test_argmax_Plots: Contains the most voted sequence from the samples in Test_Plots.
5) Test2_Plots: Samples for the test sequences (ancestors). In this case they contain the sequences sampled using the MAP estimated of the logits.
6) Test2_argmax_Plots: Samples for the test sequences (ancestors). In this case they contain the most likely amino acids indicated by the logits ("argmax the logits") (equation 4 in the paper)
2) If you selected **variational** guide:
1) Train_Plots: Contains information related to the inference of the train sequences (the leaves). They are samples obtained by using the MAP estimates of the logits.
2) Train_argmax_Plots: Single sequence per leaf obtained by the using the most likely amino acids indicated by the logits ("argmax the logits")
3) Test_Plots: Samples for the test sequences (ancestors). In this case they contain the sequences sampled using the full variational probability approach (equation 6 in the paper)
4) Test_argmax_Plots: Contains the most voted sequence from the samples in Test_Plots.
5) Test2_Plots == Test_Plots
6) Test2_argmax_Plots == Test_argmax_Plots
**Where are my ancestral sequences?**
- In each of the folders there should be a fasta file <dataset-name>_sampled_nodes_seq.fasta
- Each of the sequences in the file should be identified as <node-name-input-tree>//<tree-level-order>\_sample\_<sample-number>
>Node_A1//1.0_sample_0
**If this library is useful for your research please cite:**
```
@inproceedings{moreta2021ancestral,
title={Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder},
author={Moreta, Lys Sanz and R{\o}nning, Ola and Al-Sibahi, Ahmad Salim and Hein, Jotun and Theobald, Douglas and Hamelryck, Thomas},
booktitle={International Conference on Learning Representations},
year={2021}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >1.6.0 | pyro-ppl |
| >1.78 | biopython |
| >1.0.1 | pandas |
| >3.3.4 | matplotlib |
| >3.1.1 | ete3 |
| >0.6.1 | dgl |
| >0.3.3 | dill |
| >=0.11.2 | seaborn |
| >0.4.4 | pytorch-ignite |
| >1.5.4 | scipy |
| >0.24.1 | scikit-learn |
| >0.5.2 | umap-learn |
| >=4.3.1 | gdown |
| >2.0.0 | ProDy |
| >=5.15.7 | PyQt5 |
| >=4.9.1 | lxml |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7, <4 | Python |
نحوه نصب
نصب پکیج whl draupnir-0.0.27:
pip install draupnir-0.0.27.whl
نصب پکیج tar.gz draupnir-0.0.27:
pip install draupnir-0.0.27.tar.gz