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crisprbact-0.3.9
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مشاهده بیشترتوضیحات
Tools to design and analyse CRISPRi experiments
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | crisprbact-0.3.9 |
| نام | crisprbact |
| نسخه کتابخانه | 0.3.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | David Bikard |
| ایمیل نویسنده | david.bikard@pasteur.fr |
| آدرس صفحه اصلی | https://gitlab.pasteur.fr/dbikard/crisprbact |
| آدرس اینترنتی | https://pypi.org/project/crisprbact/ |
| مجوز | GPL-3.0 |
# CRISPRbact
**Tools to design and analyse CRISPRi experiments in bacteria.**
CRISPRbact currently contains an on-target activity prediction tool for the Streptococcus pyogenes dCas9 protein.
This tool takes as an input the sequence of a gene of interest and returns a list of possible target sequences with the predicted on-target activity. Predictions are made using a linear model fitted on data from a genome-wide CRISPRi screen performed in E. coli (Cui et al. Nature Communications, 2018). The model predicts the ability of dCas9 to block the RNA polymerase when targeting the non-template strand (i.e. the coding strand) of a target gene.
## Getting Started
### Installation
For the moment, you can install this package only via PyPI
#### PyPI
```console
$ pip install crisprbact
$ crisprbact --help
```
```
Usage: crisprbact [OPTIONS] COMMAND [ARGS]...
Options:
-v, --verbose
--help Show this message and exit.
Commands:
predict
```
### API
Using this library in your python code.
```python
from crisprbact import on_target_predict
guide_rnas = on_target_predict("ACCACTGGCGTGCGCGTTACTCATCAGATGCTGTTCAATACCGATCAGGTTATCGAAGTGTTTGTGATTGTTTGCCGCGCGCGTGGCGAAGGCCCGTGATGAAGGAAAAGTTTTGCGCTATGTTGGCAATATTGATGAAG")
for guide_rna in guide_rnas:
print(guide_rna)
```
_output :_
```
{'target': 'TCATCACGGGCCTTCGCCACGCGCG', 'guide': 'TCATCACGGGCCTTCGCCAC', 'start': 82, 'stop': 102, 'pam': 80, 'ori': '-', 'target_id': 1, 'pred': -0.4719254873780802, 'off_targets_per_seed': []}
{'target': 'CATCACGGGCCTTCGCCACGCGCGC', 'guide': 'CATCACGGGCCTTCGCCACG', 'start': 81, 'stop': 101, 'pam': 79, 'ori': '-', 'target_id': 2, 'pred': 1.0491308060379676, 'off_targets_per_seed': []}
{'target': 'CGCGCGCGGCAAACAATCACAAACA', 'guide': 'CGCGCGCGGCAAACAATCAC', 'start': 63, 'stop': 83, 'pam': 61, 'ori': '-', 'target_id': 3, 'pred': -0.9021152826078697, 'off_targets_per_seed': []}
{'target': 'CCTGATCGGTATTGAACAGCATCTG', 'guide': 'CCTGATCGGTATTGAACAGC', 'start': 29, 'stop': 49, 'pam': 27, 'ori': '-', 'target_id': 4, 'pred': 0.23853258873311955, 'off_targets_per_seed': []}
```
### Command line interface
#### Predict guide RNAs activity
Input the sequence of a target gene and this script will output candidate guide RNAs for the S. pyogenes dCas9 with predicted on-target activity.
```console
$ crisprbact predict --help
```
```
Usage: crisprbact predict [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
from-seq Outputs candidate guide RNAs for the S.
from-str Outputs candidate guide RNAs for the S.
```
##### From a string sequence:
The target input sequence can be a simple string.
```console
$ crisprbact predict from-str --help
```
```
Usage: cli.py predict from-str [OPTIONS] [OUTPUT_FILE]
Outputs candidate guide RNAs for the S. pyogenes dCas9 with predicted on-
target activity from a target gene.
[OUTPUT_FILE] file where the candidate guide RNAs are saved. Default =
"stdout"
Options:
-t, --target TEXT Sequence file to target [required]
-s, --off-target-sequence FILENAME
Sequence in which you want to find off-
targets
-w, --off-target-sequence-format [fasta|gb|genbank]
Sequence in which you want to find off-
targets format [default: genbank]
--help Show this message and exit.
```
```console
$ crisprbact predict from-str -t ACCACTGGCGTGCGCGTTACTCATCAGATGCTGTTCAATACCGATCAGGTTATCGAAGTGTTTGTGATTGTTTGCCGCGCGCGTGGCGAAGGCCCGTGATGAAGGAAAAGTTTTGCGCTATGTTGGCAATATTGATGAAG guide-rnas.tsv
```
output file `guide-rnas.tsv` :
No `seq_id` is defined since it is from a simple string.
```
target PAM position prediction seq_id
TCATCACGGGCCTTCGCCACGCGCG 80 -0.4719254873780802 N/A
CATCACGGGCCTTCGCCACGCGCGC 79 1.0491308060379676 N/A
CGCGCGCGGCAAACAATCACAAACA 61 -0.9021152826078697 N/A
CCTGATCGGTATTGAACAGCATCTG 27 0.23853258873311955 N/A
```
You can also pipe the results :
```console
$ crisprbact predict from-str -t ACCACTGGCGTGCGCGTTACTCATCAGATGCTGTTCAATACCGATCAGGTTATCGAAGTGTTTGTGATTGTTTGCCGCGCGCGTGGCGAAGGCCCGTGATGAAGGAAAAGTTTTGCGCTATGTTGGCAATATTGATGAAG | tail -n +2 | wc -l
```
##### From a sequence file
```console
$ crisprbact predict from-seq --help
```
```
Usage: cli.py predict from-seq [OPTIONS] [OUTPUT_FILE]
Outputs candidate guide RNAs for the S. pyogenes dCas9 with predicted on-
target activity from a target gene.
[OUTPUT_FILE] file where the candidate guide RNAs are saved. Default =
"stdout"
Options:
-t, --target FILENAME Sequence file to target [required]
-f, --seq-format [fasta|gb|genbank]
Sequence file to target format [default:
fasta]
-s, --off-target-sequence FILENAME
Sequence in which you want to find off-
targets
-w, --off-target-sequence-format [fasta|gb|genbank]
Sequence in which you want to find off-
targets format [default: genbank]
--help Show this message and exit.
```
- Fasta file (could be a multifasta file)
```console
$ crisprbact predict from-seq -t /tmp/seq.fasta guide-rnas.tsv
```
- GenBank file
```console
$ crisprbact predict from-seq -t /tmp/seq.gb -f gb guide-rnas.tsv
```
- Off-targets
```console
predict from-seq -t data-test/sequence.fasta -s data-test/sequence.gb guide-rnas.tsv
```
##### Output file
```
target_id target PAM position prediction target_seq_id seed_size off_target_recid off_target_start off_target_end off_target_pampos off_target_strand off_target_feat_type off_target_feat_start off_target_feat_end off_target_feat_strand off_target_locus_tag off_target_gene off_target_note off_target_product off_target_protein_id
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 1388198 1388209 1388209 +
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 2244514 2244525 2244525 + CDS 2243562 2244720 -1 NRG857_10810 COG1174 ABC-type proline/glycine betaine transport systems, permease component putative transport system permease YP_006120510.1
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 4160984 4160995 4160995 + CDS 4160074 4161406 -1 NRG857_19625 hslU COG1220 ATP-dependent protease HslVU (ClpYQ), ATPase subunit ATP-dependent protease ATP-binding subunit HslU YP_006122267.1
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 4534189 4534200 4534200 +
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 548804 548815 548804 -
1 TGATCCAGGCATTTTTTAGCTTCAT 835 0.47949500169043713 NC_017634.1:2547433-2548329 8 NC_017634.1 786462 786473 786462 - CDS 785384 786470 1 NRG857_03580 COG2055 Malate/L-lactate dehydrogenases hypothetical protein YP_006119079.1
```
## Contributing
### Clone repo
```console
$ git clone https://gitlab.pasteur.fr/dbikard/crisprbact.git
```
### Create a virtualenv
```console
$ virtualenv -p python3.7 .venv
$ . .venv/bin/activate
$ pip install poetry
```
### Install crisprbact dependencies
```console
$ poetry install
```
### Install hooks
In order to run flake8 and black for each commit.
```console
$ pre-commit install
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.17,<2.0 | numpy |
| >=7.0,<8.0 | click |
| >=1.75,<2.0 | biopython |
| >=0.16.0,<0.17.0 | rope |
| >=1.0.0,<2.0.0 | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7,<4.0 | Python |
نحوه نصب
نصب پکیج whl crisprbact-0.3.9:
pip install crisprbact-0.3.9.whl
نصب پکیج tar.gz crisprbact-0.3.9:
pip install crisprbact-0.3.9.tar.gz