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Sync main #67

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7ba31f9
Merge pull request #9 from Goosang-Yu/dev_goosang
Jinyeong-Yang Jan 25, 2024
6e2f733
Merge pull request #10 from Goosang-Yu/main
Jinyeong-Yang Jan 25, 2024
fbd7fea
🐛 Bug fixed
Jinyeong-Yang Jan 29, 2024
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Jinyeong-Yang Jan 29, 2024
cf9c867
bug fixed
Jinyeong-Yang Jan 29, 2024
13ec947
Merge pull request #66 from Jinyeong-Yang/main
Goosang-Yu Jan 29, 2024
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69 changes: 18 additions & 51 deletions README.md
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Expand Up @@ -61,75 +61,42 @@ GenET was developed for anyone interested in the field of genome editing. Especi

## Example: Prediction of prime editing efficiency by DeepPrime
![](docs/en/assets/contents/en_1_4_1_DeepPrime_architecture.svg)
DeepPrime is a prediction model for evaluating prime editing guideRNAs (pegRNAs) that target specific target sites for prime editing ([Yu et al. Cell 2023](https://doi.org/10.1016/j.cell.2023.03.034)). DeepSpCas9 prediction score is calculated simultaneously and requires tensorflow (version >=2.6). DeepPrime was developed on pytorch.
DeepPrime is a prediction model for evaluating prime editing guideRNAs (pegRNAs) that target specific target sites for prime editing ([Yu et al. Cell 2023](https://doi.org/10.1016/j.cell.2023.03.034)). DeepSpCas9 prediction score is calculated simultaneously and requires tensorflow (version >=2.6). DeepPrime was developed on pytorch. For more details, please see the [documentation](https://goosang-yu.github.io/genet/).

```python
from genet.predict import DeepPrime

seq_wt = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
seq_ed = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'

pegrna = DeepPrime('Test', seq_wt, seq_ed, edit_type='sub', edit_len=1)
pegrna = DeepPrime('SampleName', seq_wt, seq_ed, edit_type='sub', edit_len=1)

# check designed pegRNAs
>>> pegrna.features
>>> pegrna.features.head()
```

| | ID | WT74_On | Edited74_On | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | type_sub | type_ins | type_del | Tm1 | Tm2 | Tm2new | Tm3 | Tm4 | TmD | nGCcnt1 | nGCcnt2 | nGCcnt3 | fGCcont1 | fGCcont2 | fGCcont3 | MFE3 | MFE4 | DeepSpCas9_score |
| - | ---- | -------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | -------- | -------- | -------- | -------- | ------- | ------- | --------- | -------- | --------- | ------- | ------- | ------- | -------- | -------- | -------- | ------ | ----- | ---------------- |
| 0 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxxxCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 7 | 35 | 42 | 34 | 1 | 1 | 1 | 0 | 0 | 16.19097 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 5 | 16 | 21 | 71.42857 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |
| 1 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxxCCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 8 | 35 | 43 | 34 | 1 | 1 | 1 | 0 | 0 | 30.19954 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 6 | 16 | 22 | 75 | 45.71429 | 51.16279 | \-10.4 | \-0.6 | 45.96754 |
| 2 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 9 | 35 | 44 | 34 | 1 | 1 | 1 | 0 | 0 | 33.78395 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 6 | 16 | 22 | 66.66667 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |
| 3 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxCACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 10 | 35 | 45 | 34 | 1 | 1 | 1 | 0 | 0 | 38.51415 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 70 | 45.71429 | 51.11111 | \-10.4 | \-0.6 | 45.96754 |
| 4 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 11 | 35 | 46 | 34 | 1 | 1 | 1 | 0 | 0 | 40.87411 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 63.63636 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |
| 5 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 12 | 35 | 47 | 34 | 1 | 1 | 1 | 0 | 0 | 40.07098 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 58.33333 | 45.71429 | 48.93617 | \-10.4 | \-0.6 | 45.96754 |
| | ID | Spacer | RT-PBS | PBS_len | RTT_len | RT-PBS_len | Edit_pos | Edit_len | RHA_len | Target | ... | deltaTm_Tm4-Tm2 | GC_count_PBS | GC_count_RTT | GC_count_RT-PBS | GC_contents_PBS | GC_contents_RTT | GC_contents_RT-PBS | MFE_RT-PBS-polyT | MFE_Spacer | DeepSpCas9_score |
| --- | ---- | -------------------- | ------------------------------------------------- | ------- | ------- | ---------- | -------- | -------- | ------- | ------------------------------------------------- | --- | --------------- | ------------ | ------------ | --------------- | --------------- | --------------- | ------------------ | ---------------- | ---------- | ---------------- |
| 0 | SampleName | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ... | \-340.105 | 5 | 16 | 21 | 71.42857 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |
| 1 | SampleName | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ... | \-340.105 | 6 | 16 | 22 | 75 | 45.71429 | 51.16279 | \-10.4 | \-0.6 | 45.96754 |
| 2 | SampleName | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ... | \-340.105 | 6 | 16 | 22 | 66.66667 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |
| 3 | SampleName | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ... | \-340.105 | 7 | 16 | 23 | 70 | 45.71429 | 51.11111 | \-10.4 | \-0.6 | 45.96754 |
| 4 | SampleName | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ... | \-340.105 | 7 | 16 | 23 | 63.63636 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 |


Next, select model PE system and run DeepPrime
```python
pe2max_output = pegrna.predict(pe_system='PE2max', cell_type='HEK293T')

>>> pe2max_output.head()
```
| | Target | Spacer | RT-PBS | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | PE2max_score |
| - | ------------------------------------------------- | ------------------------------ | ---------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | ------------ |
| 0 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | 0.904907 |
| 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | 2.377118 |
| 2 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | 2.613841 |
| 3 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | 3.643573 |
| 4 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | 3.770234 |


The previous function, ```pe_score()```, is still available for use. However, please note that this function will be deprecated in the near future.
```python
from genet import predict as prd

# Place WT sequence and Edited sequence information, respectively.
# And select the edit type you want to make and put it in.
#Input seq: 60bp 5' context + 1bp center + 60bp 3' context (total 121bp)

seq_wt = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
seq_ed = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
alt_type = 'sub1'

df_pe = prd.pe_score(seq_wt, seq_ed, alt_type)
df_pe.head()
```
| | Target | Spacer | RT-PBS | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | PE2max_score |
| - | ------------------------------------------------- | ------------------------------ | ---------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | ------------ |
| 0 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | 0.904907 |
| 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | 2.377118 |
| 2 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | 2.613841 |
| 3 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | 3.643573 |
| 4 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | 3.770234 |



It is also possible to predict other cell lines (A549, DLD1...) and PE systems (PE2max, PE4max...).

```python
df_pe = prd.pe_score(seq_wt, seq_ed, alt_type, sID='MyGene', pe_system='PE4max', cell_type='A549')
```

| | ID | PE2max_score | Spacer | RT-PBS | PBS_len | RTT_len | RT-PBS_len | Edit_pos | Edit_len | RHA_len | Target |
| - | ---- | ------------ | -------------------- | ---------------------------------------------- | ------- | ------- | ---------- | -------- | -------- | ------- | ------------------------------------------------- |
| 0 | SampleName | 0.904387 | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... |
| 1 | SampleName | 2.375938 | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... |
| 2 | SampleName | 2.61238 | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... |
| 3 | SampleName | 3.641537 | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... |
| 4 | SampleName | 3.768321 | AAGACAACACCCTTGCCTTG | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... |


Please send all comments and questions to [email protected]
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