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PreKO: Precise KO system

Project description

PreKO: Precise KO system

Analysis pipeline for PreKO project

Installation

PreKO에 대한 분석 프로그램은 아래와 같이 설치할 수 있습니다.

pip install PreKO

InDelSearcher: Cas9 nuclease indel analyzer

InDelSearcher는 target sequence에서 indel frequency를 분석하고 계산해주는 파이프라인이다. 특히, high-throughput screening 데이터에서 barcode에 따른 indel frequency를 분석하는 것에 특화되어 있다.

분석을 위해서, 아래와 같이 barcode와 target sequence 정보가 담긴 csv 파일이 필요하다.

Barcode Target_region Reference_sequence
TTTGCTGTGAGCACTGCTG TTGTGAACATAGATCCATTTTTCTTGG CTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTNNNNNNNNTTTGCTGTGAGCACTGCTGT
TTTGGACGTCATAGTGAGA TCCAGATAGTCATCAACTTTTTGTTGG CTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTNNNNNNNNTTTGGACGTCATAGTGAGAT
TTTGGCTATCTGCACGTGC GTGGGGGGCCTGGGGCCTGGAGCCTGG CTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTNNNNNNNNTTTGGCTATCTGCACGTGCG
TTTGATGCGCATCTCTACG CCCAGGCAAAACTGCAGTTTTACCTGG CTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTNNNNNNNNTTTGATGCGCATCTCTACGC
TTTGACTCGAGTCTCTCAC ACGAGGTGGCCCTGGGGGGCCCCCTGG CTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTNNNNNNNNTTTGACTCGAGTCTCTCACA

barcode 파일과 분석할 FASTQ 파일이 있다면, InDelSearcher를 이용한 분석을 할 수 있다.

import pandas as pd
from PreKO.indel import InDelSearcher

# Setting: required information
DIR_FASTQ   = f'NGS_data/Cas9_FASTQ_combined/'
barcode     = f'ref_Small_Cas9_KO_Lib.csv'

sample_name = f'HCT_Cas9_R1_Day7'
fastq_file  = f'{DIR_FASTQ}/{sample_name}.fastq'

ids = InDelSearcher()

# Run and show summary
df_summary = ids.run(strFq=fastq_file, barcode=barcode, sample_name=sample_name, thread=25)
df_summary.to_csv(f'InDel_Summary_{sample_name}.csv')

InDelSearcher.run의 output (pd.DataFrame)은 아래와 같이 나온다.

Barcode Target_region intSwitching intNumOfTotal intNumOfIns intNumOfDel intNumofCom IndelFrequency
ATGTGATCATGC CAGGAAAAAATATGTGCTATGGAGGGG 477 9425 771 4997 145 62.7374
GACTCTCGTCGA TTTTAGAGAATATTCACCGTGTCACGG 1034 10495 726 5355 9 58.02763
GACTGATACTGT ACAAAGTCAACTGCCTTCAAACAAGGG 3630 13708 2968 7268 176 75.95565
GCTGCGCGCACT CTTCTATAACAAGAAATCTGATGTGGG 726 10402 451 5919 50 61.7189
TCGCTGTGACTC CCGCGCCGCGCGTTACCTTCCGCGGGG 479 9150 1225 4749 44 65.77049

이를 to_csv() 등의 함수로 저장해서 이후 분석에 사용한다.

Environments

These codes were tested in Ubuntu 22.04 LTS environments.

Requirements

  • Python >= 3.8
  • biopython
  • pandas
  • numpy
  • pydantic
  • tqdm

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