DNA Sequences Embedding about dnabert

[Zhihan1996]

Hi,

I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.

For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

[iamysk]

Hi,

How do I get embeddings of multiple sequences at once? I tried with a list of sequences, but the output is always a 1x768 vector.

Thanks.

[aliakay]

Hello, I just want to import the DNATokenizer from the transformers, but there is wrong with 'ImportError: cannot import name 'DNATokenizer'', can you help me to solve this? Google doesn't tell me anything about it...

You should clone it in right directory, because DNATokenizer is inside of the DNABERT folder.

Try this,

!git clone https://github.com/jerryji1993/DNABERT
%cd DNABERT
!python3 -m pip install --editable .
%cd examples
!python3 -m pip install -r requirements.txt

and run import in this directory.
cd "DNABERT/examples"
then it should import the DNATokenizer.

[ChengkuiZhao]

Hi,
I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.
For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

Dear Zihian,

I would like to use DNABert to get short dna sequence representation in order to keep sequencing relationship instead of using one hot encoder method and I want to put this values to another model for my desire, is it good use directly embedding representation which is output[1] or attention score would be work also for representation?

I did some research on the output of this model, the output[0] is the last_hidden_state (https://huggingface.co/docs/transformers/main_classes/output), I saw people used output[0][ : , 0 , : ] which means the 768 dimension vector for the 'CLS' in the last hidden layer for the following model, and that works for me. I think the attention score is not for the output representation.

[Zhihan1996]

Hi,

Yes, you can do this. Please refer to the line 420 of https://github.com/jerryji1993/DNABERT/blob/master/examples/run_finetune.py. The variable logits stands for the embedding of the DNA sequence. You can directly use it.

[elbasir]

Hi,
Thanks for your answer. I have checked the embedding using the variable logits and I found out the embedding size is only 2-dimensional vector for each DNA sequence. I have a sequence that's longer than 500 bps, would it be possible to extend the embedding size or you think the current embedding size is enough to represent a long DNA sequence?

[elbasir]

Thanks a lot!

[maiskovich]

Hi,

I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.

For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

I tried this code and when running it inside a for loop I was always getting the process killed as it was using too much memory, I needed to put the prediction part inside with torch.no_grad():, It ended looking like this:

from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
with torch.no_grad():
        model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
        model_input = torch.tensor(model_input, dtype=torch.long)
        model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one
        
        output = model(model_input)```

[asimokby]

Hi,

I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.

For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

Thank you this is very helpful!

If you run this snippet in a script that lives in the parent directory, the same level as the examples folder, you may run into some problems.

I had to change the following two imports in modeling_albert.py:
from transformers.configuration_albert import AlbertConfig from transformers.modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer

to the following:

from transformers.models.albert.configuration_albert import AlbertConfig from transformers.models.bert.modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer

Also, I made a change to the snippet to get it to work. I changed the following import statement:

from transformers import BertModel, BertConfig, DNATokenizer

to

from src.transformers import DNATokenizer from transformers import BertModel, BertConfig

[ChengkuiZhao]

Hello, I just want to import the DNATokenizer from the transformers, but there is wrong with 'ImportError: cannot import name 'DNATokenizer'', can you help me to solve this? Google doesn't tell me anything about it...

[ChengkuiZhao]

Hello, I just want to import the DNATokenizer from the transformers, but there is wrong with 'ImportError: cannot import name 'DNATokenizer'', can you help me to solve this? Google doesn't tell me anything about it...

You should clone it in right directory, because DNATokenizer is inside of the DNABERT folder.

Try this,

!git clone https://github.com/jerryji1993/DNABERT %cd DNABERT !python3 -m pip install --editable . %cd examples !python3 -m pip install -r requirements.txt

and run import in this directory. cd "DNABERT/examples" then it should import the DNATokenizer.

Actually I didn't use the DNATokenizer, and used the BERTTokenizer instead. The code also works, is this two way different and make much difference?
Thank you so much for your reply.

[aliakay]

Hello, I just want to import the DNATokenizer from the transformers, but there is wrong with 'ImportError: cannot import name 'DNATokenizer'', can you help me to solve this? Google doesn't tell me anything about it...

You should clone it in right directory, because DNATokenizer is inside of the DNABERT folder.
Try this,
!git clone https://github.com/jerryji1993/DNABERT %cd DNABERT !python3 -m pip install --editable . %cd examples !python3 -m pip install -r requirements.txt
and run import in this directory. cd "DNABERT/examples" then it should import the DNATokenizer.

Actually I didn't use the DNATokenizer, and used the BERTTokenizer instead. The code also works, is this two way different and make much difference? Thank you so much for your reply.

As far as understand,DNATokenizer is specifically trained by DNA Sequences on the other side, BERT Tokenizer is tokenizer for sentences, so you will get different input values when you run both tokenizer which effect your output.

[ChengkuiZhao]

Hello, I just want to import the DNATokenizer from the transformers, but there is wrong with 'ImportError: cannot import name 'DNATokenizer'', can you help me to solve this? Google doesn't tell me anything about it...

You should clone it in right directory, because DNATokenizer is inside of the DNABERT folder.
Try this,
!git clone https://github.com/jerryji1993/DNABERT %cd DNABERT !python3 -m pip install --editable . %cd examples !python3 -m pip install -r requirements.txt
and run import in this directory. cd "DNABERT/examples" then it should import the DNATokenizer.

Actually I didn't use the DNATokenizer, and used the BERTTokenizer instead. The code also works, is this two way different and make much difference? Thank you so much for your reply.

As far as understand,DNATokenizer is specifically trained by DNA Sequences on the other side, BERT Tokenizer is tokenizer for sentences, so you will get different input values when you run both tokenizer which effect your output.

OK, I will try your way to install this requirements. These days, I just write the code on my own and didn't install it, hope the installation will work.

[aliakay]

Hi,

I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.

For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

Dear Zihian,

I would like to use DNABert to get short dna sequence representation in order to keep sequencing relationship instead of using one hot encoder method and I want to put this values to another model for my desire, is it good use directly embedding representation which is output[1] or attention score would be work also for representation?

[WENHUAN22]

Dear authors,

I have a question about obtaining embedding vectors of my data.
For example, the first record in my dataset is "AATCTA ATCTAG TCTAGC CTAGCA".

May I use
model.embeddings.word_embeddings.weight[0]
as the embedding vector of my first sample?
Is there any difference between the method you introduce above (output[1]) and this one?

And after I have embedding vectors I am going to build a classifier on them. It will be nice if you tell me whether this thought is correct or not.

[palset]

Hi,

I am sorry that I was wrong in the last response. The 2-dimensional logits here is essentially the classification results of the given sequence, where each dimension stands for the probability of this sequence belongs to each class. The embedding for each sequence should be a 768-dimensional vector. You can achieve it by

import torch
from transformers import BertModel, BertConfig, DNATokenizer

dir_to_pretrained_model = "xxx/xxx"

config = BertConfig.from_pretrained('https://raw.githubusercontent.com/jerryji1993/DNABERT/master/src/transformers/dnabert-config/bert-config-6/config.json')
tokenizer = DNATokenizer.from_pretrained('dna6')
model = BertModel.from_pretrained(dir_to_pretrained_model, config=config)

sequence = "AATCTA ATCTAG TCTAGC CTAGCA"
model_input = tokenizer.encode_plus(sequence, add_special_tokens=True, max_length=512)["input_ids"]
model_input = torch.tensor(model_input, dtype=torch.long)
model_input = model_input.unsqueeze(0)   # to generate a fake batch with batch size one

output = model(model_input)

Here the output[1] is the embedding of the input sequence.

For the current version, if you have sequences longer than 512, then you need either cut it to 512 or split it into multiple pieces of 512 lengths and concatenate their embedding together.

I see that the model_input, which is returned by the tokenizer.encode_plus, does not have padding on the left or right, even though the input size is < 512. If I add padding on the right, the embedding generated by DNABERT changes. So, what according to you is the correct format? Should I manually add padding on the right, or just ignore it?
Thanks for the great work!