CRISPR Library

1 Project Summary

Technical Overview

CRISPR/Cas9 high-throughput screening enriches sgRNAs of target candidate genes through positive or negative selection, identifying genes of interest. The workflow: lentiviral library construction, virus infection, cell screening, gDNA extraction, NGS library prep, sequencing and bioinformatics analysis.

Main Applications of CRISPR/Cas9 High-Throughput Screening

• Screening of drug targets for drugs with significant efficacy but unknown mechanisms;
• Exploring potential targets for tumor therapy to aid drug development;
• Investigating resistance genes in anti-tumor drug resistance;
• Identifying synthetic lethal genes for combination therapy strategies.

Experimental Workflow

1.1 Library Preparation

DNA samples undergo shear, end repair, A-tailing, adapter ligation, cleanup, PCR enrichment, cleanup, and QC to construct Illumina sequencing libraries.

1.2 Sequencing

Sequencing on Illumina platform. FASTQ is the output format containing read sequences and quality scores. Each read has four lines:

Line 1Starts with "@", contains read ID and optional description separated by a space. The ID contains colon-separated fields.

Line 2Base sequence (A, T, C, G, N where N = uncertain base).

Line 3Starts with "+", separates sequence from quality scores.

Line 4Quality scores in ASCII format, corresponding to each base in Line 2.

1.3 Data Analysis

In this report, CRISPR library screening data were analyzed using MAGeCKFlute, a comprehensive computational pipeline for CRISPR screen data analysis. MAGeCKFlute integrates the core functionalities of both MAGeCK and MAGeCKFlute, enabling systematic data processing and interpretation. Specifically, it supports sgRNA‑level quality control, gene‑level positive and negative selection scoring, as well as downstream functional enrichment analysis to facilitate biological interpretation of screening results.

2 Quality Control

Sequencing Quality and Error Rate Distribution

Sequencing error rate is related to base quality, affected by the sequencer, reagents, and sample quality. For Illumina platforms:

• First few bases typically have lower quality due to initial focusing lag of the sequencer's fluorescence sensor.
• Error rate increases and quality decreases as sequencing progresses due to incomplete fluorophore cleavage and de-phasing.

Quality Score and Error Rate Conversion

Base quality score (Q) and error rate (P) are related by:

Raw Data Filtering

Raw sequencing data contains adapter-contaminated and low-quality reads. These are filtered to obtain clean reads for downstream analysis.

Data Processing Steps:

1. Remove read pairs with length < 50 bp;
2. Remove read pairs if N base ratio > 10%;
3. Remove read pairs with Q20 < 80% (Q20 = base quality score ≥ 20).

Quality Control Statistics

Clean Summary Overview

Raw Data Quality Distribution

Quality distribution of raw sequencing data for each sample, including base quality and GC content distribution.

Base Quality & Error Rate 2 images

base quality and error rate Control 1

base quality and error rate Treatment 1

Raw Reads Classification 2 images

Raw reads classification Control 1

Raw reads classification Treatment 1

3 Sample Correlation Analysis

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer malesuada erat ut velit consequat, sed tempor lorem faucibus. Praesent vitae lacus nec sapien tincidunt vulputate.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed non purus vitae neque facilisis tincidunt. Morbi tristique sapien at lorem posuere, vitae malesuada augue elementum. Fusce varius, nisl at interdum consequat, neque libero faucibus arcu, eget tincidunt lorem ipsum vitae erat.

Correlation Criteria

• Lorem ipsum: correlation parameter lorem ipsum dolor sit amet
• Lorem ipsum: correlation value consectetur adipiscing elit
• Quality Control (QC): Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer tincidunt malesuada velit, sed faucibus lorem posuere non. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae.

Sample Correlation Analysis

Sample Correlation Matrix

Correlation Heatmap

Correlation heatmap of all samples. Darker colors indicate higher correlation between samples.

Correlation Heatmap 1 image

4 Statistical Analysis

Sample Read Statistics

Extract sgRNA sequences from reads, align to sgRNA library reference, and summarize alignment rates, sgRNA/gene counts, coverage and uniformity.

Demo.countsummary Data Overview

sgRNA Alignment Statistics

This section summarizes the alignment performance of the sgRNA library, including the number of sgRNAs successfully mapped to the reference (Mapped sgRNAs), the number of sgRNAs that failed to map (Missed sgRNAs), and the distribution of sgRNA coverage as measured by the Gini coefficient.

Mapped sgRNA Statistics

Missed sgRNA Statistics

Gini Coefficient Distribution

Density Distribution Plot

Displays the alignment performance of the sgRNA library, including the number of sgRNAs successfully mapped to the reference (Mapped sgRNAs), the number of sgRNAs that failed to map (Missed sgRNAs), and the distribution of sgRNA coverage as measured by the Gini coefficient.

Displays the distribution of sgRNAs that failed to map to the reference across individual samples. Missed sgRNAs typically arise from sequencing errors or library variations. A high proportion of missed sgRNAs may compromise the reliability of the screening results.

The Gini coefficient was used to assess the uniformity of sgRNA library coverage. A lower Gini coefficient indicates a more even distribution of sgRNA read counts, whereas a higher value suggests that a subset of sgRNAs is disproportionately represented, reflecting uneven library coverage. Ideally, the Gini coefficient should be below 0.2.

Visualizes the sgRNA distribution across different experimental groups. By comparing the read count profiles between treatment and control samples, it enables assessment of screening pressure‑induced changes in sgRNA representation.

5 Screening Results

Genes are ranked by the RRA algorithm. A lower RRA score indicates greater essentiality for cell growth.

Negative Selection Results

Demo.gene_summary Negative Selection Gene Ranking

Field Descriptions:
id: Gene name;
num: Number of sgRNAs for this gene;
score: Negative selection RRA score;
p-value: Negative selection p-value;
fdr: Negative selection FDR;
rank: Negative selection rank;
goodsgrna: Number of effective sgRNAs;
lfc: Log2 fold change (negative selection).

sgRNA Rank Plot

sgRNA Scatter Plot

sgRNA Enrichment Details

RankView: RRA scores of the top-ranked genes from the negative selection analysis, with highly ranked genes highlighted.

ScatterView: Distribution of log2 fold change values for each sgRNA of the top-ranked genes from the negative selection analysis across the treatment and control groups.

sgRNAView: Detailed log2 fold change values for each sgRNA of the top-ranked genes from the negative selection analysis. Positive values are shown in red and negative values in blue.

Positive Selection Results

Demo.gene_summary Positive Selection Gene Ranking