DRAUPNIR: "Beta library version for performing ASR using a tree-structured Variational Autoencoder"

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 python environment .yaml

https://github.com/LysSanzMoreta/DRAUPNIR_ASR/tree/main/draupnir/env/Python3_9

Draupnir pip install

pip install draupnir

Example

See Draupnir_example.py

Which guide to use?

By experience, use delta_map, since 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 optimize 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

     

     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 results 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 marginal probability approach (equation 5 in the paper).
   2. Train_argmax_Plots: Single sequence per leaf obtained by the using the most likely amino acids indicated by the marginal 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 estimates 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 MAP 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 from sampling from the variational posterior (equation 6 in the paper).
   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 _sampled_nodes_seq.fasta

• Each of the sequences in the file should be identified as //_sample_

  -Node-name-input-tree: Original name of the node in the given input tree

  -Tree-level-order: Position of the node in tree-level order in the tree

  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}
}

This software is not for commercial use.

Do not hesitate to give input on how to improve the documentation of this library

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