Skip to content

Latest commit

 

History

History
112 lines (78 loc) · 7.39 KB

Reproducible_Workflow.md

File metadata and controls

112 lines (78 loc) · 7.39 KB
Raw

Pretraining:

Under the folder titled "mutformer_pretraining," first open "mutformer_pretraining_data generation_(with dynamic masking op).ipynb," and run through the code segments (if using colab, runtime options: Hardware Accelerator-None, Runtime shape-Standard), selecting the desired options along the way, to generate eval and test data, as well as begin the constant training data generation with dynamic masking.

Once the data generation has begun, open "mutformer_run_pretraining.ipynb," and in a different runtime, run the code segments there (if using colab, runtime options: Hardware Accelerator-TPU, Runtime shape-High RAM if available, Standard otherwise) to start the training.

Finally, open "mutformer_run_pretraining_eval.ipynb" and run all the code segments there (if using colab, runtime options: Hardware Accelerator-TPU, Runtime shape-Standard) in another runtime to begin the parallel evaluation operation.

You can make multiple copies of the data generation and run_pretraining scripts to train multiple models at a time. The evaluation script is able to handle parallel evaluation of multiple models at once.

To view pretraining graphs or download the checkpoints from GCS, use the notebook titled “mutformer_processing_and_viewing_pretraining_results.”

Finetuning

For finetuning, there is only one set of files for four finetuning strategies, so at the top of each notebook, select the desired mode to use (MRPC for paired strategy, MRPC_w_preds for MRPC with external predictions, RE for single sequence strategy, and NER for pre residue strategy).

Under the folder titled "mutformer_finetuning," first open "mutformer_finetuning_data_generation.ipynb," and run through the code segments (if using colab, runtime options: Hardware Accelerator-None, Runtime shape-Standard), selecting the desired options along the way, to generate train,eval,and test data.

Once the data generation has finished, open "mutformer_finetuning_benchmark.ipynb," and in a different runtime, run through the code segments there (if using colab, runtime options: Hardware Accelerator-TPU, Runtime shape-High RAM if available, Standard otherwise). At the bottom of the notebook, there are currently two different benchmarking modes specified, though more can be added according to the same format. Choose and run one of these benchmarking strategies.

Finally, alongside running mutformer_run_finetuning, open "mutformer_finetuning_benchmark_eval_predict.ipynb" and run all the code segments there (if using colab, runtime options: Hardware Accelerator-TPU, Runtime shape-Standard) in another runtime to begin the parallel evaluation operation.

Input Data format guidelines:

General format:

Each residue in each sequence should be separated by a space, and to denote the actual start and finish of each entire sequence, a "B" should be placed at the start of each sequence and a "J" at the end of the sequence prior to trimming/splitting.

for pretraining, datasets should be split into "train.txt", "eval.txt", and "test.txt" for finetuning, datasets should be split into "train.tsv", "dev.tsv", and "test.tsv"

During finetuning, whenever splitting was required, we placed the mutation at the most center point possible, and the rest was trimmed off.

Pretraining:

We have included our pretraining data in this repository as an asset, called "pretraining_data.zip"

Alternatively, a google drive link: www.openbioinformatics.org/mutformer/pretraining_data.zip

The format should be a txt with each line containing one sequence. Each sequence should be trimmed/split to a maximum of a fixed length (in our case we used 1024 amino acids).

Example file:

B M E T A V I G V V V V L F V V T V A I T C V L C C F S C D S R A Q D P Q G G P G J
B M V S S Y L V H H G Y C A T A T A F A R M T E T P I Q E E Q A S I K N R Q K I Q K 
L V L E G R V G E A I E T T Q R F Y P G L L E H N P N L L F M L K C R Q F V E M V N 
G T D S E V R S L S S R S P K S Q D S Y P G S P S L S F A R V D D Y L H J

Finetuning training (not inference)

Single Sequence Classification (RE)

The format should be a tsv file with each line containing (tab delimited):

  1. mutated protein sequence
  2. label (1 for pathogenic and 0 for benign).

Example file:

V R K T T S P E G E V V P L H Q V D I P M E N G V G G N S I F L V A P L I I Y H V I D A N S P L Y D L A P S D L H H H Q D L    0
P S I P T D I S T L P T R T H I I S S S P S I Q S T E T S S L V V T T S P T M S T V R M T L R I T E N T P I S S F S T S I V    0
G Q F L L P L T Q E A C C V G L E A G I N P T D H L I T A Y R A Q G F T F T R G L S V R E I L A E L T G R K G G C A K G K G    1
P A G L G S A R E T Q A Q A C P Q E G T E A H G A R L G P S I E D K G S G D P F G R Q R L K A E E M D T E D R P E A S G V D    0

Per Residue Classification (NER)

The format should be a tsv file with each line containing (tab delimited):

  1. mutated protein sequence
  2. per residue labels
  3. mutation position (index; if the 5th residue is mutated the mutation position would be 4) ("P" for pathogenic and "B" for benign).

The per residue labels should be the same length as the mutated protein sequence. Every residue is labeled as "B" unless it was a mutation site, in which case it was labeled either "B" or "P." The loss is calculated on only the mutation site.

Example file:

F R E F A F I D M P D A A H G I S S Q D G P L S V L K Q A T    B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B    16
A T D L D A E E E V V A G E F G S R S S Q A S R R F G T M S    B B B B B B B B B B B B B B B P B B B B B B B B B B B B B B    16
G K K G D V W R L G L L L L S L S Q G Q E C G E Y P V T I P    B B B B B B B B B B B B B B B P B B B B B B B B B B B B B B    16
E M C Q K L K F F K D T E I A K I K M E A K K K Y E K E L T    B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B    16

Paired Sequence Classification (MRPC)

The format should be a tsv file with each line containing (tab delimited):

  1. label (1 for pathogenic and 0 for benign)
  2. reference sequence
  3. mutated sequence

Example file:

1    D W A Y A A S K E S H A T L V F H N L L G E I D Q Q Y S R F    D W A Y A A S K E S H A T L V F Y N L L G E I D Q Q Y S R F
0    S A V P P F S C G V I S T L R S R E E G A V D K S Y C T L L    S A V P P F S C G V I S T L R S W E E G A V D K S Y C T L L
1    L L D S S L D P E P T Q S K L V R L E P L T E A E A S E A T    L L D S S L D P E P T Q S K L V H L E P L T E A E A S E A T
0    L A E D E A F Q R R R L E E Q A A Q H K A D I E E R L A Q L    L A E D E A F Q R R R L E E Q A T Q H K A D I E E R L A Q L

Paired Sequence Classification With External Data (MRPC_w_ex_data)

The format should be a tsv file with each line containing (tab delimited):

  1. label (1 for pathogenic and 0 for benign)
  2. reference sequence
  3. mutated sequence
  4. external data (float values separated by spaces)

Example file:

1    D W A Y A A S K E S H A T L V F H N L L G E I D Q Q Y S R F    D W A Y A A S K E S H A T L V F Y N L L G E I D Q Q Y S R F    0.6 0.137 0.5 0.9812
0    S A V P P F S C G V I S T L R S R E E G A V D K S Y C T L L    S A V P P F S C G V I S T L R S W E E G A V D K S Y C T L L    0.0 0.101 0.1 0.0001
1    L L D S S L D P E P T Q S K L V R L E P L T E A E A S E A T    L L D S S L D P E P T Q S K L V H L E P L T E A E A S E A T    1.0 0.986 0.8 0.9995
0    L A E D E A F Q R R R L E E Q A A Q H K A D I E E R L A Q L    L A E D E A F Q R R R L E E Q A T Q H K A D I E E R L A Q L    0.0 0.012 0.0 0.0