Page Text: The identifier in this case would be “Non_Target”.
Number of sgRNAs per gene:
Specify the number of sgRNAs targeting a single gene (e.g. 6 in case of the GeCKO library). If this number varies across the library, choose the number that applies to the majority of library entries. Non-targeting controls, miRNAs or other control entries in the library are excluded.
2 Description of the PinAPL-Py Analysis output
The PinAPL-Py output is structured by logical order into tabs and subtabs on the results page. In addition, all output can be downloaded via the “Download Results” button as a single .zip file. Images are saved both as high-resolution .png as well as as .svg vector graphics which can be further processed in Adobe Illustrator or similar image processing software. Tables are saved as raw text (.txt), but can be manually opened with Excel and saved as Excel spreadsheets. For convenience, PinAPL-Py can convert tables on-the-fly (see the “Table Format” parameter on the configuration page), at the cost of additional computation time. NOTE for Windows users: To view text files (.txt/.tsv/.csv), Notepad++ is recommended
NOTE: When the analysis is run with two or more replicate samples for a condition, PinAPL-Py will show an additional sample for that condition (named "_avg") where results are averaged across the replicates.
2.1 Gene Ranking Results
Gene Rankings
This tab contains the results of the gene ranking analysis in a sortable table. The columns are:
Gene: Name of gene (as defined in the library file)
Gene Score: Value of the computed gene metric score
Gene Score p-value: Estimated (one-sided) p-value of the gene score, based on a permutation test where sgRNAs are randomly assinged to genes.
Significant: Statistical significance of the gene score.
# sgRNAs:Number of sgRNAs targeting the gene
# Signif. sgRNAs: Number of sgRNAs targeting the particular gene that reached statistical significance in the sgRNA ranking
By default, the table is sorted by p-value. You can sort by other columns (ascending or descending) by clicking on the respective column headers (arrows).
Scatterplots
This tab plots the gene score for each gene (alphabetically sorted on the x-axis). Significant scores are plotted in green. Non-targeting controls (if present) are plotted in orange. The selector at the top can be used to highlight a particular gene of interest (After clicking the selection box, you can type first letter of the gene name to find the gene more quickly).
p-Value Distribution
This tab shows the distribution of p-values from the gene ranking analysis. Typically, a bimodal distribution is seen, with a small bar on the left end and a large bar on the right end.
sgRNA Efficacy:
This tab shows information about the overall efficacy of sgRNAs targeting the same gene. Genes are categorized by the number of targeting sgRNAs that reached statistically significant enrichment/depletion. Genes having no significant sgRNAs are omitted.
2.2 sgRNA Ranking Results
Rankings
This tab contains the results of the sgRNA ranking analysis in a sortable table. The columns are:
sgRNA: Identifier of sgRNA
Gene: Name of target gene
Counts: Normalized read count
Control Mean: Average normalized read count of this sgRNA in the control
Control StDev: Standard deviation of normalized read count in the control
Fold Change: The ratio of normalized read count in the sample to the control average
p-value: p-value (one-sided) of the enrichment/depletion of this sgRNA compared to the control
Significant: Statistical significance of the enrichment/depletion of this sgRNA
By default, the table is sorted by p-value. You can sort by other columns (ascending or descending) by clicking on the respective column headers (arrows).
Plots
Treatment vs Control
Scatterplots of normalized sgRNA read counts in the sample versus the average normalized count in the controls. The fraction reaching significant enrichment/depletion (dependent on screen type) compared to the control is plotted in green. Non-targeting controls (if present) are plotted in orange. The selector at the top can be used to highlight a particular gene of interest (After clicking the selection box, you can type first letter of the gene name to find the gene more quickly). After highlighting a particular gene, the IDs of the corresponding sgRNAs can be displayed using the checkbox on the far right. Highlighting of non-targeting controls can be switched on or off with the checkbox next to the gene selector.
Density Plots **** NEW in v2.9 ***
Same data as the previous tab, but showing a density plot of the sgRNA abundance.
Volcano Plots
This tab plots sgRNA p-value against fold-change. The fraction reaching significant enrichment/depletion (dependent on screen type) compared to the control is plotted in green. Non-targeting controls (if present) are plotted in orange. The selector at the top can be used to highlight a particular gene of interest (After clicking the selection box, you can type first letter of the gene name to find the gene more quickly). After highlighting a particular gene, the IDs of the corresponding sgRNAs can be displayed using the checkbox on the far right. Highlighting of non-targeting controls can be switched on or off with the checkbox next to the gene selector. p-values are capped at 1e-16 for technical purposes.
z-Score Plots
This tab shows the fold-change for each sgRNA ranked from lowest to highest. The z-Score is the normalized deviation from the mean read count. The fraction reaching significant enrichment/depletion (dependent on screen type) compared to the control is plotted in green. Non-targeting controls (if present) are plotted in orange. The selector at the top can be used to highlight a particular gene of interest (After clicking the selection box, you can type first letter of the gene name to find the gene more quickly). After highlighting a particular gene, the IDs of the corresponding sgRNAs can be displayed using the checkbox on the far right. Highlighting of non-targeting controls can be switched on or off with the checkbox next to the gene selector.
p-Values
This tab shows the distribution of p-values from the sgRNA enrichment/depletion analysis.
Read Count Distribution
This tab shows information about the statistical distribution of sgRNA read counts.
Left: Lorenz curves and Gini coefficients: The Lorenz curve visualizes the distribution of reads, showing what fraction of sgRNAs (green) or genes (blue) is represented by what fraction of reads. The Gini coefficient quantifies the difference of this distribution from a perfectly even distribution. A perfectly even distribution would result in a diagonal curve (Gini coefficient = 0) and would indicate a complete absence of selection in the screen. An extremely uneven distribution results in a flat curve (Gini coefficient = 1) and would indicate extreme selection (only a single sgRNA/gene is represented in the sequencing data). Thus, the more selective the conditions of the screen are, the closer will the Gini coefficient approach 1.
Right: Boxplots and histograms: Boxplots and histograms for the read counts per sgRNA (green) or gene (blue), respectively. Outliers are omitted for visualization purposes.
Bottom: Summary: Descriptive statistics are summarized. sgRNA/gene representation measures the number of sgRNAs/genes detected by at least one read count in the sample (as percentage of the full library).
Clusters
This tab shows a cluster analysis (heatmap) of all samples in the dataset, based on the most variable or most abundant/depleted sgRNAs (as chosen on the configuration page). Log10 normalized read counts are color-coded from lowest (yellow) to highest (red).
Replicate Correlation:
Scatterplots showing the normalized sgRNA read counts in one replicate of each condition versus another. Pearson and Spearman correlation coefficients are reported. Non-targeting controls (if present) are plotted in orange.
Read Count Dispersion:
This tab shows the distribution of read counts in the control samples only. These data are used to estimate the parameters of the statistical model describing the distribution of sgRNA read counts throughout the rest of the dataset.
Left: Read Count Overdispersion: This plot visualizes the degree of overdispersion in the data, i.e. the degree by which the variance of read counts exceeds the mean (as typically seen in next-generation sequencing datasets). In case of significant overdispersion, a negative binomial model is chosen over a Poisson model.
Right: Mean/Variance Model: This plot shows shows a regression of log overdispersion against log mean. This is required to compute the parameters of the statistical model.
2.3 Alignment Results
Summary
This tab shows the sequencing depth (number of total reads) per sample as well as the fractions of reads successfully or unsuccessfully aligned to the reference library. Alignment Unique: Only a single match found in the reference library. Alignment Tolerated: Multiple matches found in the reference library, but the difference in matching score between the best and second-best was above the ambiguity threshold. Alignment Ambiguous: Multiple matches found in the reference library, and the difference in matching score between the best and second-best was below the ambiguity threshold. Alignment Failed: No match found in reference library. Reads with failed and ambiguous alignment are discarded prior to the subsequent enrichment/depletion analysis.
Alignment Statistics
Left: Mapping Quality: Histogram of the overall quality by which the reads mapped to the reference library. Reads that uniquely align to a single library sequence yield a high mapping quality score. Reads that ambiguously align to multiple library sequences or that do not align to any library sequence yield a low mapping quality score. For more detailed information about computation of the mapping quality score, please refer to the Bowtie2 manual .
Right: Alignment Analysis: Barplot showing the primary (best) and secondary (second-best) alignment scores achieved for each read. If a read perfectly aligns to only one library sequence, its primary alignment score will be maximal, and its secondary alignment score will be 0. If a read aligns ambiguously to multiple library sequences, its secondary alignment score will be close to its primary alignment score. If a read does not align to any library sequence, both its primary and secondary alignment scores will be 0. The fraction of reads marked in red is being discarded prior to enrichment/depletion analysis.
Bottom: Summary: This text summarizes the the success of the alignment. For an explanation of the parameter settings reported at the bottom of the page, see the ALIGNMENT section).
Sequence Quality:
This tab provides analyses for sequence quality control (produced by fastqc). For the full fastqc output, click the “See full report” link
Upper Left: Per Base Quality: This plot shows the quality distribution for every base position in the read. y-axis is sequence quality score (Phred). Preferably, the majority of the read is in the green area.
Upper Right: Per Sequence Quality: This plot shows a sequence quality histogram. y-axis shows number of reads. Preferably, sequence quality should peak at a score >= 35.
Lower Left: GC Content: This plot shows a histogram of the the GC content. y-axis shows number of reads.
Lower Right: Per Base Sequence Variation: This plot shows the fractions of T, C, A and G for every base position in the read. A balanced mix is typically only seen in the 20 bp sgRNA sequence.
2.4 Run Info
Output Log
This shows the program execution log. If you experience technical difficulties during your run, browse the log for error messages as they can provides clues for trouble-shooting.
Configuration
This file shows the parameter settings used for the analysis run.
Sample Names
This table shows file names and the corresponding sample names. Replicates of the same condition are numbered automatically.
3 REFERENCES
Anders,S. and Huber,W. (2010) Differential expression analysis for sequence count data. Genome Biol. , 11 , R106.
Doench,J.G. et al. (2016) Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. , 34 , 184 –191.
Li,W. et al. (2014) MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. , 1 –12.