tRNA Inference

In [1]:

from dnallm import load_config
from dnallm import load_model_and_tokenizer, DNAInference

from dnallm import load_config from dnallm import load_model_and_tokenizer, DNAInference

tRNADetector¶

In [2]:

# load config
configs = load_config("./inference_model_config_tRNADetector.yaml")

model_name = "zhangtaolab/tRNADetector"
model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'],  source="modelscope")

predictor = DNAInference(
	model=model,
	tokenizer=tokenizer,
	config=configs
)

# load config configs = load_config("./inference_model_config_tRNADetector.yaml") model_name = "zhangtaolab/tRNADetector" model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="modelscope") predictor = DNAInference( model=model, tokenizer=tokenizer, config=configs )

Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNADetector
Model files are stored in /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNADetector
Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNADetector
Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNADetector

Importing `MambaCache` from `transformers.cache_utils` is deprecated and will be removed in a future version. Please import it from `transformers` or `transformers.models.mamba.cache_mamba` instead.
The fast path is not available because one of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)` is None. Falling back to the mamba.py backend. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d

15:04:25 - dnallm.inference.inference - INFO - Using device: cpu

In [3]:

seq = ['AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG', 'AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA']
results = predictor.infer_file(seq,  evaluate=False)

seq = ['AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG', 'AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA'] results = predictor.infer_file(seq, evaluate=False)

Encoding inputs:   0%|          | 0/2 [00:00<?, ? examples/s]

Inferring: 100%|██████████| 1/1 [00:09<00:00,  9.68s/it]

In [4]:

for i in results:
	sequence = results[i]['sequence']
	label = results[i]['label']
	score = results[i]['scores'][label]
	print(f'input sequence:{sequence}\n',
	   	f'predict label:{label}\n', 
		f'predict score:{score}\n',
		f'*'*20)

for i in results: sequence = results[i]['sequence'] label = results[i]['label'] score = results[i]['scores'][label] print(f'input sequence:{sequence}\n', f'predict label:{label}\n', f'predict score:{score}\n', f'*'*20)

input sequence:AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG
 predict label:tRNA
 predict score:0.9999682903289795
 ********************
input sequence:AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA
 predict label:Partial tRNA
 predict score:1.0
 ********************

In [ ]:

tRNAPointer¶

In [ ]:

# load config
configs = load_config("./inference_model_config_tRNAPointer.yaml")

model_name = "zhangtaolab/tRNAPointer"
model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'],  source="modelscope")

predictor = DNAInference(
	model=model,
	tokenizer=tokenizer,
	config=configs
)

# load config configs = load_config("./inference_model_config_tRNAPointer.yaml") model_name = "zhangtaolab/tRNAPointer" model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="modelscope") predictor = DNAInference( model=model, tokenizer=tokenizer, config=configs )

Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNAPointer
Model files are stored in /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNAPointer
Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNAPointer
Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNAPointer
Downloading Model from https://www.modelscope.cn to directory: /Users/forrest/.cache/modelscope/hub/models/zhangtaolab/tRNAPointer
15:04:44 - dnallm.inference.inference - INFO - Using device: cpu

In [ ]:

seq = ['AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG', 'AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA']

seq_token = []
for _ in seq:
	seq_token.append([base for base in _])

results = predictor.infer_file(seq_token,  evaluate=False)

seq = ['AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG', 'AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA'] seq_token = [] for _ in seq: seq_token.append([base for base in _]) results = predictor.infer_file(seq_token, evaluate=False)

Encoding inputs:   0%|          | 0/2 [00:00<?, ? examples/s]

Inferring: 100%|██████████| 1/1 [00:08<00:00,  8.74s/it]

In [7]:

for i in results:
	sequence = ''.join(results[i]['sequence'])
	label = results[i]['label']
	try:
		start = label.index("B-tRNA")
		end = len(label) - 1 - label[::-1].index("I-tRNA")
		tRNA_sequence = sequence[start:end+1]
		print(f'input sequence:{sequence}\n',
	   	      f'tRNA start index in sequence:{start}\n',
		      f'tRNA end index in sequence:{end}\n',
		      f'tRNA sequence:{tRNA_sequence}\n',
			  f'*'*20)
	except:
		print(f'input sequence:{sequence}\n',
	   	      'No tRNA found\n',
			  f'*'*20)

for i in results: sequence = ''.join(results[i]['sequence']) label = results[i]['label'] try: start = label.index("B-tRNA") end = len(label) - 1 - label[::-1].index("I-tRNA") tRNA_sequence = sequence[start:end+1] print(f'input sequence:{sequence}\n', f'tRNA start index in sequence:{start}\n', f'tRNA end index in sequence:{end}\n', f'tRNA sequence:{tRNA_sequence}\n', f'*'*20) except: print(f'input sequence:{sequence}\n', 'No tRNA found\n', f'*'*20)

input sequence:AAGAAAGCTCAAATAGTATACGAAGAACTCGAAGCTAAGCAACTGTGAAGAGAAATTAAGTAGCTACAATTAGGTTATAAATAATTTGATTTCTACTCTAACTGTGACGTGGGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCATTTTTTTATTTTTTTTTAGAATTCTACTTTTTCTAAAATTGACCCTTTAATTTTGTATTTATATTTCTTTTATAATGTATATGCATTCTGCATTTTATTTTTCCTTTACATTTTTTCTTATATAATGTAAGTTATGCATTCTGCATTTTCTTTTGTCTTTTTTTTTTCTTATAAGTGGTTGG
 tRNA start index in sequence:111
 tRNA end index in sequence:183
 tRNA sequence:GGGATGTAGCTCAGATGGTAGAGCGCTCGCTTAGCATGCGAGAGGTACGGGGATCGATACCCCGCATCTCCAT
 ********************
input sequence:AAAACCCCAACTAGCTAGCATCGATCGAGCTAGCATGCATCGATCGATCGATCGATCGATCGATCGATCGAACACCCCGCGCGTAGCTACGGCTCAGAGCATCGATGCGCAGTCGAGCCGGGGGGGACATCGATCGATCGATCGATCGAGTCGACGATCGATCGAGCATATAATCGAGTCGACTGATCGATCGAGCGTACGATCGATCGATCGATGCATCCCCGATCGATCGATCGATCTTATAACACACACACACACACACGGAAAA
 No tRNA found
 ********************

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