CRISPR-AnalyzeR.org offers you a web-based analysis plattform for your pooled CRISPR Screens.

× CRISPRAnalyzeR 1.50 is out.

Last Update: 2018-01-16 Version 1.50

× Please note that this is a live demonstration only.



CRISPRAnalyzeR is a user-friendly analysis suite for pooled CRISPR/Cas9 screens


Explore your Data, Explore your Analysis.

CRISPRAnalyzeR is a web-based analysis platform for pooled CRISPR screens. CRISPRAnalyzeR was developed with user experience in mind and provides you with a one-in-all data analysis workflow. And once you are finished, you can download all the data as well as your analysis as an interactive HTML report.


CRISPRAnalyzeR offers the following features

  • Interactive
  • FASTQ/NGS Quality
  • Easy-to-use
  • Screening Quality
  • 8 different Hit-Calling methods
  • Gene Annotation
  • In-depth information about Genes and sgRNAs from 26 external data ressources
  • Essential Genes
  • Gene Ontology
  • KEGG Analysis
  • Protein Interactions
  • Gene Set Analysis
  • Fully-interactive offline Report



Download CRISPRAnalyzeR for offline use


You can use our online web service, but also download CRISPRAnalyzeR to install it on your local computer or within your lab/institute. CRISPRAnalyzeR is open-source and free for non-commercial use, please check out the download pages below.

CRISPRAnalyzeR can be downloaded so you can install it on your computer


For further information please check our Github page:


Or download CRISPRAnalyzeR directly:

Data Upload  Upload your FASTQ Screening Files and sgRNA Library


As a first step, you need to upload your sequencing files and your sgRNA library file - both are required for the data analysis.
Sequencing files can either be read count files ( .txt - please check the format) or zipped FASTQ files ( .fastq.gz)

Sample Data

In case you like to try out CRISPRAnalyzeR without your own data, we have some sample data ready for you. All you need to do is download one of the sample data files, extract them - everything you need is included. Each data package consists of a sgRNA library file (.fasta) and NGS rawd ata (.fastq.gz) or read count files (.txt).

Focused Screen (12000 sgRNAs, custom library)

This 12k custom sgRNA library was published before in

F. Heigwer*, T. Zhan*, M. Breinig, J. Winter, D. Brügemann, S. Leible, M. Boutros,
CRISPR library designer (CLD): software for multispecies design of single guide RNA libraries,
Genome Biol., 2016, DOI:10.1186/s13059-016-0915-2
                                                                                       

Sample Data with Read Count Files

In this package you will find 4 readcount files and a sgRNA library FASTA file.

File Name Description
PBS-Replicate1.txt Read Count file for untreated replicate 1
PBS-Replicate2.txt Read Count file for untreated replicate 2
TRAIL-Replicate1.txt Read Count file for TRAIL-treated replicate 1
TRAIL-Replicate2.txt Read Count file for TRAIL-treated replicate 2
pilotscreen.fasta sgRNA library FASTA file with 12000 sgRNAs
Download Read Count Package  0.5 MB

Sample Data with NGS FASTQ.gz Files

In this package you will find 4 .fastq.gz NGS files and a sgRNA library FASTA file.

File Name Description
PBS-Replicate1.fastq.gz NGS FASTQ.gz file for untreated replicate 1
PBS-Replicate2.fastq.gz NGS FASTQ.gz file for untreated replicate 2
TRAIL-Replicate1.fastq.gz NGS FASTQ.gz file for TRAIL-treated replicate 1
TRAIL-Replicate2.fastq.gz NGS FASTQ.gz file for TRAIL-treated replicate 2
pilotscreen.fasta sgRNA library fasta file with 12000 sgRNAs
Download Raw Data Package  1.38 GB


Whole Genome Essential Genes Screen (90000 sgRNAs, TKOv1 library)

This screening data was published in

Steinhart,Z. et al. (2016)
Genome-wide CRISPR screens reveal a Wnt-FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors.
Nat. Med.
                                                                                              

Sample Data with Read Count Files

In this package you will find 3 read count files and a sgRNA library fasta file.

File Name Description
TKO_readcount_initial1 Read Count file for Day 0
TKO_readcount_final1 Read Count file after 20 cell doublings (replicate 1)
TKO_readcount_final2 Read Count file after 20 cell doublings (replicate 2)
FASTA_TKO_90K_library.fasta sgRNA library FASTA file for this screen with 90000 sgRNAs
Download TKO Read Count Package  4.1 MB



Data Upload

You can upload NGS FASTQ.gz sequencing data or Read Count Files (.txt) in addition to your sgRNA library file (.fasta).

sgRNA Library File in FASTA format

The sgRNA library file must be provided in a FASTA format. This library file is not only used to identify the genes to which your sgRNAs belong but also allows CRISPRAnalyzeR to annotate your screen correctly.

Example files for the most common sgRNA libraries can be downloaded from below at the end of the help section.

>ENSG00000006042_0_4299.561
GGAGCCCTCTGAGTTAGAAC

Sequencing Data (NGS .FASTQ files)

Your screening data can be uploaded either as gzipped NGS FASTQ (.fastq.gz) or tab-separated Readcount(.txt) files. In case you upload FASTQ files, CRISPRAnalyzeR uses bowtie2 to map the FASTQ reads against the provided sgRNA library file. More advanced users can adjust bowtie2 parameters in the FASTQ Options Box at Step 3. Since FASTQ files are large, we would ask you only upload gzip-compressed files as they are usually provided by your sequencing machine. In this case, the files should end with .fastq.gz. Otherwise, please compress them using gzip first.

Tab-Separated Read Count Files

You can upload tab-separated read count files, in which every line contains a unique sgRNA identifier as well as the number of reads assigned to it. Please note that the sgRNA identifiers must be the same as in the sgRNA library file you provide. Since CRISPRAnalyzeR performes the the normalization, you can upload raw read count files.

Example of a tab-separated read count file:

sgRNA Count
AAK1_104_0 0
AAK1_105_0 597
AAK1_106_0 145
AAK1_107_0 0
AAK1_108_0 0
AAK1_109_0 142

Extraction of your uploaded FASTQ.gz files

In case you upload FASTQ files instead of Read count files, your FASTQ data is extracted before the identification of your single sgRNA reads. For this purpose, CRISPRAnalyzeR needs to know how your FASTQ files do look like and how it can identify the barcode of each single sgRNA. By default, this barcode is the 20 nt target sequence as it is defined in the sgRNA library file you uploaded. To extract this information from your sequencing data, CRISPRAnalyzeR requires a so-called regular expression.


For your convenience, CRISPRAnalyzeR already provides you with pre-defined settings for the most common libraries and vector systems. As an alternative for more advanced users, you can also type in your own regular expression.




Pre-Made sgRNA library files for download

CRISPRAnalyzeR offers you pre-made sgRNA library fasta files for commonly used CRISPR libraries.

Please email us if you want your sgRNA library file to be distributed with CRISPRAnalyzeR.

Library Name Lab Pubmed ID Addgene Download
CLD Benchmarking Boutros 27013184 NA FASTA
Gecko V2 Zhang 25075903 A+B FASTA A only FASTA B only FASTA
Gecko V2 MOUSE Zhang 25075903 A only FASTA B only FASTA
Torronto KnockOut Library (TKO) Moffat 26627737 90K FASTA 85K FASTA
Brunello Doench 26780180 FASTA
CRISPRa / CRISPRi Weissmann 25307932 not available yet
Human Lentiviral sgRNA library high cleavage activity Sabatini 26472758 185K FASTA
Human Lentiviral sgRNA sub libraries Sabatini 24336569 not available yet



sgRNA Library - How To

sgRNA library files must be in FASTA format and include a unique sgRNA identifier as well as the guide target sequence (typically 19-21 nt) in 5'->3' direction.

An example of a FASTA file structure

>ENSG00000006042_0_4299.561
GGAGCCCTCTGAGTTAGAAC 
>ENSG00000006042_5_4299.588
GAAGATGCCTCGTAAGGCCA 
>ENSG00000006042_6_4299.588
GAGATGCCTCGTAAGGCCAT 

sgRNA library file structure

A single item within the FASTA file consists of two lines. In this case it is the the sgRNA identifier with its target sequence. The first line is used to identify the item. It always starts with a > followed by the sgRNA identifier. The second line provides the corresponding sgRNA target sequence without the PAM sequence .

How to select the correct Regular Expressions for sgRNA libraries

Regular Expressions are used to extract the gene identifier from your sgRNA identifier. This is important as we want to provide you not only a gene-based analysis, but also additional visualizations and information about your candidate gene. To find a matching regular expression, you need to know the structure of such an expression and of course, the structure of your sgRNA identifier as it is used within your library file. Regular expressions for our pre-made libraries can be found below .


CRISPRAnalyzeR provides you with pre-defined regular expressions for commonly used vector systems. Alternatively, see below how to find the correct expression for your custom library.

How to find the right Regular Expression

A Regular Expression is used to detect both the gene and sgRNA identifier for each sgRNA within your library file.

Note the brackets which are necessary. Regular Expression for the pre-made library FASTA files are provided below.


NGS Sequencing Files (.fastq.gz)

You can also provide raw data NGS files directly from your sequencing machine in a FASTQ format. Since FASTQ data can be large, the files which must be compressed using GZIP (.fastq.gz file ending). CRISPRAnalyzeR extracts all information from your FASTQ NGS file and performs the mapping against the sgRNA library file. Please make sure the FASTQ extraction pattern in Step3 matches the vector system you used for screening.

Example of a FASTQ file

@M01100:63:000000000-AMJC7:1:1101:21768:1401 1:N:0:1
TTGGATTCTTGTGGAAAGGACGAAACACCGTTCCCTGCAGCCCTCATGCGTTTTAGAGCTAGAAAT
+
CCCCCGGGGGGGGGGGGGGGGGGFGGGGGGGGGGGGGGFGFDGGGGGGGGGGGGGGGGG
Please provide FASTQ files in a gzipped format (.fastq.gz)

CRISPRAnalyzeR needs to know which plasmid you used

In order to find the sgRNA target sequence (which is used as a barcode to identify the sgRNA), CRISPRAnalyzeR needs to know where to find it within your sequencing data. For this purpose, regular expressions are used to extract this information from your sequencing rawdata.
You can have a look at the plasmid map of the vector you used and try to identify the left and right flanking sequences next to the sgRNA target sequence (as illustrated below). Alternatively you can use one of the pre-defined settings depending on your vector system.

You can find the required regular expression for your screening vector the following way

Regular Expressions are used to extract the sgRNA target sequence barcode from your NGS FASTQ file. To find a matching regular expression, you need to know the sequence or name of the vector you used for performing the lentiviral transduction. All you need to do is select a couple of bases flanking your sgRNA target sequence on the left and right side. CRISPRAnalyzeR provides with pre-defined settings for the most common screening systems (see below), but you can use your custom system as well.
In this case, select CUSTOM in Step3 and enter a custom regular expression by the addition of 2-4 bases of both the left and right flanking sequences on the left and right site of the bracket. For the above shown example you could use the following regular expression for a sgRNA target sequence of either 20 or 21 nt in length:

The expression to detect the sgRNA target sequence is shown in blue color. Don't hesitate to ask us for help. CACC (.{20,21}) GTTTT


You can modify the pre-defined settings or add your own custom regular expression

Just tick the checkbox and enter a custom regular expression.


You can use the pre-defined settings for the most common screening vectors:

Plasmid Name Lab Addgene ID Expression required for Step3
Lenticrisp V2 Zhang 52961 Default Setting ACC(.{20,21})G
Lentiguide (Puro) Zhang 52963 Default Setting ACC(.{20,21})G
Human Lentivirus Library V1 Haoquan Wu 69763 GTTT(.{20})GT
pLCKO (TKO Library) Moffat 73311 ACCG(.{20,21})G
pU6-sgRNA EF1Alpha-puro-T2A-BFP (CRISPRa/i) Weissman 60955, 62217, 60956 Default Setting GTTG(.{20})G


Step 1: Please select the screening library

Please select which screening library you would like to analyze

You can select pre-made settings for various CRISPR Screening libraries available from Addgene.
But you can always analyze any type of pooled CRISPR screen by selecting CUSTOM



Step 1a: Upload Your sgRNA Library File

You can download pre-made sgRNA library FASTA files from the help section.

Please upload your sgRNA library file that contains all sgRNAs used in the screen. It must be in FASTA format.


The regular expression depends on how you designed the sgRNA identifiers within your sgRNA library file.
For more information please see the help where you can also download pre-made FASTA library files.

Some Examples:

sgRNA Identifier
ENSG00000166535_30_12678.2422
Regular Expression
^(.+?)(_.*)$

An example from your sgRNA library

This is how CRISPRAnalyzeR detects the gene from your sgRNA identifier.

Please select the regular expression from the left dropdown menu or type in your own. You selected the correct regular expression in the case your gene is highlighted in blue colour.


Expert Options

You can add/modify the regular expression

Do you want to use a custom regular expression for the FASTA library?

This will override the default settings to the left and is only for expert users.

Please check out the help or the tutorials section to find further information.




Optimize sgRNA Library File

By default, CRISPRAnalyzeR optimizes and performs consistency checks. If you have problems, you can disable the optimization procedure.

Step 2: Upload Your Sequencing Files

New to CRISPRAnalyzeR? Try our sample data:



The sample data can be used with the default settings.

You can upload the following files:

Read Count Files (.txt)
Already mapped read count files in .txt tab-separated format
NGS Sequencing Files (.fastq.gz)
Compressed FASTQ files as they are provided by the sequencer.

Please note: You can upload multiple files at once.

Rename Files
In order to provide a good user experience, please rename your files to include only letters and numbers.

Example of a read count file (.txt)

The sgRNA identifier needs to be TAB-separated from the counts.

sgRNA	Count
ENSG00000053900_GAAAGCAATGAGATCCCGCT	28
ENSG00000053900_GAAGCAATGAGATCCCGCTT	62
ENSG00000053900_GAAGCGGGATCTCATTGCTT	92

Example of a FASTQ file (not compressed)

The file needs be GZIP-compressed.

@M01100:47:000000000-AH40C:1:1101:12289:2057 1:N:0:4
AACACCGTCAGTGTGCTTGCCCCACTGTTTTAGAGCTAGAAATAGCAAGTT
+
GGGGGGGGGGDGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
@M01100:47:000000000-AH40C:1:1101:8758:2058 1:N:0:4
AAACACCGGTTTTGAAACTGCGGACACGTTTAGAGCTAGAAATAGCAAGTTA
+
GGGGGGGEGGGGGGGGGDGGFGGGEEGGFFGGFFCFFF=AFF<CEFFF@EFE

Step 3: Set FASTQ Options (if FASTQ files uploaded)

This is relevant if you upload compressed FASTQ files. These defaults should work fine in most cases.


If you have a really low overall read count, you can try to go down with these parameters and accept mapping mismatches.

Expert Options


You can modify / add your own regular expression


Do you want to use a custom regular expression?

This will override the default settings to the left and is only for expert users.

Please check out the help or the tutorials section to find further information.


You can override the warning for low alignment rates

By default, CRISPRAnalyzeR prevents you from data analysis in case the alignment rate is below 30%. However you can override this to also use samples of such low quality.

This is only for expert users.

Do you really want to override the alignment quality check?


Create a FASTQ data quality report

By default, CRISPRAnalyzeR generates a FASTQ data quality report, which can take some time. However, you can turn it off in case you are in a hurry.


Perform FASTQ extraction in fast mode

By default, CRISPRAnalyzeR processes FASTQ files using a specialized tool for faster processing. In case you have issues with FASTQ extraction (e.g. all read counts are 0), you can try to disable the fast processing of FASTQ files and revert back to the old method.




Data Review   Review your Data and Download Rawdata Files


Review your uploaded data and download generated read count files or the quality report of your FASTQ files.

How can I download read count files?

You can download all read count files CRISPRAnalyzeR created for you.

CRISPRAnalyzeR gives you the opportunity to download the read count files for your samples.

In case you uploaded FASTQ.gz data, the read count files have been generated by mapping your sequencing data against your sgRNA library. If you come back to CRISPRAnalyzeR, you can then just upload the read count files together with your sgRNA library file, which is much faster.

The read count files are tab-separated text files and come within a gzip archive.

How can I download a quality report for my FASTQ.gz sequencing data?

CRISPRAnalyzeR also provides an extensive quality report for your sequencing data.

Once you upload FASTQ.gz sequencing data, CRISPRAnalyzeR will create a sequencing data quality report. The report is generated using the Rqc package

Welliton Souza and Benilton Carvalho (2016).
Rqc: Quality Control Tool for High-Throughput Sequencing Data.
R package version 1.6.2. https://github.com/labbcb/Rqc

What information is provided in the overview table?

Feature Description
Original Filename The name of the uploaded file
Provided Nameame The name you provided after uploading the files
Type Either Fastq for sequencing files or Readcount for read count data
Size The filesize
sgRNA Extraction Ratio The ratio gives an estimate of how many sequencing reads were extracted according to the selected regular expression. In general this should be higher than 80%.
Reads Mapped To Reference CRISPRAnalyzeR maps your raw data sequencing files to your sgRNA library file and tells you, how many reads were mapped to your sgRNA library. Each sequencing read can be mapped only once (Aligned once), multiple times or it might be that a read cannot be mapped to your library at all.
Reads passed Quality Threshold You select a quality threshold for the mapping of sequencing reads to the sgRNA library. This is the percentage of reads which have passed the selected quality criteria.
Reads Used in Total This is the percentage of reads that is finally used for the data analysis after the sequencing data handling, which includes the extraction of sgRNA sequences, the mapping to the sgRNA library and the applied mapping quality threshold.



Download Read Count Files

For your convenience, just get the generated read count files.

Read count files can be used the next time - which is much faster than using FASTQ data.
Download Readcount files

Download FASTQ QC Report

Download the optional FASTQ QC report.

This report is only available in case you uploaded NGS raw data.
Download Quality Report for FASTQ files

Overview of Uploaded Data and Samples

Download Analysis Data

Download All Analysis Data

You can download all raw data form the individual hit calling methods either as a tab-separated .TSV file or as a fully formatted .XLSX Excel file. Download .TSV Download as Excel .XLSX

Download All Intermediate Analysis Data

Download .ZIP

Download All Generated Raw Data

This will download everything that has been generated and is only for advanced users. The filesize can be larger than 1 GB. Download .ZIP

Download the sgRNA Re-Evaluation File

You can download the sgRNA Re-Evaluation file that includes genomic locations and scores for each individual sgRNA of your library. Download .TSV

Setup  Define treatment groups and gene identifier


Tell CRISPRAnalyzeR which treatment groups you have, assign samples to it and adjust the gene annotation parameters.

In order to assign samples to a group, the upload of your samples needs to be completed.
You did a mistake? Don't worry, you can easily change the setup by clicking on the Change Setup button.

Why do I need to set groups?

CRISPRAnalyzeR performs hit analysis by comparing two groups, whith each group containing the corresponding samples.

All hit analysis methods that are implemented in CRISPRAnalyzeR require the use of two groups that are compared. This means, you need to assign each sample to either one of the groups you would like to compare. As an example, this could be a Day0 vs a Day12 group or a Control vs a Drug Treatment group.

How can I setup my treatment groups?

CRISPRAnalyzeR will always compare two treatment groups

You can increase the number of groups, but the minimum required number is 2. If you like you can also name each group.

How can I assign my samples to the groups?

You can assign each file to one group by clicking on the sample name. If you like to have more than one file per group, keep the CMD or CTRL button pushed while selecting the samples. Please note that your are not allowed to use the same sample in more than one treatment group.


Why do I need to set gene identifiers?

CRISPRAnalyzeR needs to know the type of gene identifier used for the screen - and can automatically convert it for you

CRISPRAnalyzeR needs to know in which organism your screen was performed and which gene identifier is used in the sgRNA library file. With this information, CRISPRAnalyzeR can offer you extensive gene annotation and visualizations.

CRISPRAnalyzeR expects the type of organism, the gene identifier and which identifier you would like to convert it to.

If you do not want to convert your gene identifier, please select the same identifer twice.

Supported Gene Identifiers

Gene Identifier Example
Ensembl Gene ID e.g. ENSG00000141510
EntrezGene ID (also NCBI Gene ID) e.g. 7157
HGNC ID e.g. 11998
HGNC symbol e.g. TP53
Unigene ID e.g. 7157
Uniprot Gene Name e.g. TP53


Step 1: Which treatment groups do you have?

Please set the number of treatment groups within your screen and adjust their names. You can select the treatments groups you like to compare in the Set Analysis Parameters section later.

Step 2: Assign your files to the groups

Please assign which of the uploaded samples belongs to which treatment group.
One sample can only belong to one treatment group, you are not allowed to assign two different groups to the same sample.
We strongly advise you to have replicates, however you can still run the analysis even with a single sample per group.
Please note: Analysing data without at least two replicates per sample is not advised since most analysis methods do not work properly without replicates.

You can select mutliple samples by pressing CMD or CTRL

Step 3: Adjust the gene annotation parameters

CRISPRAnalyzeR converts the gene identifier for easier usage, so please select the type of gene identifier within your sgRNA library file.
If you want to stay with your gene identifier, just select the same identifier twice. Please note: Gene ID conversion is done using the Ensembl BiomaRt service.
Please don't forget to click. You can always change the settings by clicking on the Change Setup button.

Settings  Analysis Settings


In the settings tab all important parameters for the Hit Analysis are set.
Moreover you can define control genes and select which of the groups you defined are compared.
All information from the Data Upload and Setup page remain unaffected.

How do I setup screening controls?

You can set positive and non-targeting controls which will help you to judge your screen performance

What are positive controls?

Positive controls are genes, for which the screening outcome is known. These will tell you whether your screening setup worked and are very important to judge your screening performance.

Which gene can be used as a positive controls strongly depends on the type of screen. For Viability/Dropout screen, essential genes can be used as positive control.

What are non-targeting contols?

Non-targeting controls are articicial genes, which are not present in the screened organism. Since these controls don't have any target in the screening organism, they can be used to judge the screening variability. Common non-targeting controls are GFP or random sgRNA sequences.


Adjust thresholds used for hit identification

CRISPRAnalyzeR uses different algorithms to analyze your data

For each algorithm, you can set a p-value threshold that is used to discriminate between significant and non-significant hit candidates.


Wilcox
Analysis on sgRNA read count
Wilcoxon analysis is based on a Mann-Whitney test between a random/non-targeting sgRNA set and all sgRNAs for each targeted gene. If no non-targeting controls have been set, randomly picked sgRNAs are used as test reference instead.
DESeq2
Analysis on Gene read count
For this analysis, the DESeq2 package is employed on gene-level read count to find potential hit candidates between the treatment groups.
MAGeCK
Analysis on sgRNA read count
MAGeCK analysis is performed on sgRNA readcount comparing the selected treatment groups. For this, MAGeCK is implemented as described on the MAGeCK website
edgeR
Analysis on sgRNA read count
EdgeR hit analysis is performed on sgRNA read counts followed by a gene enrichment analysis. EdgeR has been implemented as described here
Dai et al. (2014) edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens, F1000Research, 3:95.
sgRSEA
Analysis on sgRNA read count
SgRSEA (single-guide RNA Set Enrichment Analysis) has been implemented as described in the corresponding R package available at CRAN

Can I remove low / high read counts from the analysis?

If you like to remove sgRNAs with low or even high read counts from your analysis, just activate the checkbox and tell CRISPRAnalyzeR the desired threshold. Each sgRNA having a read count below/above or equal to the threshold will be removed from the analysis.


How can I analyze two groups?

CRISPRAnalyzeR will run the analysis between two groups

You can select the two groups out of all groups you have defined on the Set Groups And Identifier page.


Can I run multiple analysises?

CRISPRAnalyzeR always runs a pairwise analysis between two groups. If you want to compare additional groups, you can always come back to the Set Analysis page and click on the Change Setting button at the bottom. This allows you to change all analysis parameters and start a new analysis without uploading the data again.



Step 1: Set Controls (optional)

Controls are important to judge the screening results. Please enter the Gene Identiiers of your controls, separate multiple genes by comma (',').

Step 2: Adjust Hit Calling Thresholds

Differential Hit Analysis

CRISPRAnalyzeR supports 6 different analysis methods which will be used to analyse your screen. Please define the p-value cutoffs for each individual analysis. From our experience, the p-value threshold for DESeq2 should be set very low.

Wilcox

If you did not specify non-targeting controls Wilcox will use randomly picked sgRNAs.

DESeq2

MAGeCK

sgRSEA

EdgeR

BAGEL

Essential Genes Analysis

You can define the range in which you expect the BAGEL cutoff.

ScreenBEAM

Do you want to run ScreenBEAM data analysis? This will increase the analysis calculation time by at least three times.

You can change the default calculation parameters of ScreenBEAM. Changing these parameters will lead to increased/decreased calculation times!

Step 3: Select which Groups to Compare

CRISPRAnalyzeR will compare the following two groups. Please note that you can only compare two groups at once. If you have more treatment groups, you can always come back to this page and run pairwise analyses.

Step 4: Removing low/high Read Counts from Analysis

CRISPRAnalyzeR allows you to remove sgRNAs with low/high read counts from the entire analysis.

For a more robust analysis, we recommend to remove sgRNAs with a read count of less than 20.





Screen Quality FASTQ Sequencing Quality


In case you provided NGS Fastq files, you will find visualizations of the sequencing quality on this page.

What do the plots tell me?

CRISPRAnalyzeR provides you with a set of FASTQ quality plots once you uploaded FASTQ sequencing files

CRISPRAnalyzeR uses the Rqc Bioconductor package to provide you with FASTQ quality information.

http://www.bioconductor.org/packages/release/bioc/vignettes/Rqc/inst/doc/Rqc.html
Souza W and Carvalho B (2016). Rqc: Quality Control Tool for High-Throughput Sequencing Data.
R package version 1.8.0, https://github.com/labbcb/Rqc.

Cycle-sepcific quality plots

GC content

Shows the GC-base content for each sequencing cycle

Quality Distribution

Shows the quality score proportion per cycle. Colors are presented in a gradient Red-Blue, where red identifies calls of lower quality.

Average Quality

Describes the average quality score for each cycle of sequencing

Basecall Proportion

Describes the proportion of each nucleotide present at every cycle of sequencing.

Read Frequency and Width

Read Frequency

Shows the proportion and frequency of sequencing reads.

Read Width

Describes the length of reads within the sequencing file.



You can download a FASTQ quality report from the Data Review section

Screen Quality Basic Dataset Stats


In case you like numbers, this is the tab for you.
Here you can have a look at raw numbers of your screen and get a feeling for the quality of the screen.
In all Screening Quality plots these numbers are used for generating the plots.

Overview

Mean
The mean read count of all sgRNAs/Genes in the dataset
Median
The median read count of all sgRNAs/Genes in the dataset
Min
The minimum read count of all sgRNAs/Genes in the dataset
Max
The maximum read count of all sgRNAs/Genes in the dataset
SD
The calculated Standard Deviation of all sgRNA/Gene read count in the dataset
Missing
The number of sgRNAs/Genes which have a read count of 0. In case the uploaded sgRNA FASTA library contains more items than the screening data, these sgRNAs/Genes will reported as missing.

Readcounts

The Readcounts tab allows you to browse through your readcount data. You can also sort according to the different columns and search for specific genes or sgRNAs. On the left you can select whether you wish to browse readcounts per sgRNA or get a summed-up readcount per gene.

Distribution

Described the log2 read count distribution for all sgRNAs of each dataset.

Did you know?

You can zoom into the plot by keeping the left-mouse button pushed. Moreover, you can select/deselect any dataset by clicking on the dataset name below the plot.

Controls

If you have set either positive or non-targeting controls in the Set Analysis Parameter section, read statistics for these controls are shown.

Read Depth

Shows the read count of each gene divided by the number of sgRNAs that contributed to it. This is used to give you an idea of read count depth normalized to the number of sgRNAs. The Read Depth plot shows you how many reads fall onto every gene present in your dataset. Positive controls (if set) are highlighted in red, non-targeting control (if set) are highlighted in blue color.

Did you know?

You can zoom into this plot for a closer inspection.




Screen Quality Sequencing coverage


A gooed coverage is a key to successfull CRISPR screens. Here you can check whether all sgRNAs are still present in your samples. It shows you how many sgRNAs/Genes were not present in the screening data and how many sgRNAs per gene are present at all.

Missing sgRNAs/Genes

The missing sgRNA/Genes tab shows you how many sgRNAs/Genes were not present in the sequencing data compared to the uploaded sgRNA library file. This means the read count for this particular sgRNA or the sum of sgRNA read counts for a particular genes is 0. If a read count threshold has been applied, removed sgRNAs are NOT listed as missing.

Missing Genes
Number of Genes in which all sgRNAs were not present in the dataset compared to the provided sgRNA library.
Missing sgRNAs
Number of sgRNAs which revealed a read count of 0 in the dataset.
% of missing non-targeting sgRNAs
The percentage of sgRNAs, which belong to the non-targeting control, that were not present in the dataset.
% of missing positive control sgRNAs
The percentage of sgRNAs, which belong to the positive control, that were not present in the dataset.

Designs per Gene

Distribution of sgRNA presence for all genes in the dataset. In case all sgRNAs for all genes are present, you will expect only one bar.



Screen Quality Sample comparison


For sure your screen was done in replicates. But how well are your replicates?
This page will give you the answer as an Overview, or more detailed incase you are patient.

Overview

A fast overview

Here you will find a boxplot visualization of the log2 Foldchanges or Z-Ratios between all uploaded samples on both gene and sgRNA level. This gives you a fast overview to check for outlying samples, that might have an unexpected influence on the analysis of your data.

Pairwise Comparisons

For a more detailed view

These plots allow you to directly compare two samples by both log2-Foldchange and Z-Ratio. With this you can inspect how foldchanges differ between your screning replicates.

Scatterplot Matrices

A fast overview

The grid gives you a fast overview of the correlation of your replicates. You can view the correlation for both sgRNA and Gene read count. CRISPRAnalyzeR calculates the Pearson as well as the Spearman coefficient. For a more detailed view, have a look at the compare datasets tab.

Scatterplots

For a more detailed view

If you are interested in a more detailed view, you can select two datasets or treatment groups and compare them on either gene or sgRNA level. You can highlight positive or non-targeting controls which will shown in red or blue color respectively. Moreover you can select multiple genes which will be highlighted in orange color within the scatterplot. For gene-based levels, please tick the 'Show the plot with gene level read count'. Moreover, you can get all scatter plots with log10-transformed data.




Screen Quality Principal Component Analysis


A principal component analysis can give you first idea how similar or differential your samples are and which of the samples you provided nicely group together.

What is Principal Component Analysis

A principal component analysis is a method to reduce multi-dimensional data into two-dimensional projection by keeping the information about the variance in the dataset. It tries to identify directions, for which the variation is maximum. These directions are called principal components.

Please check out the following article, in which PCA on genomic data is nicely explained. Nature Computational Biology


Please note:
You can download the plot using the upper right menu. Moreover, you can zoom into the plot for a more detailed view.

Principal Component Analysis




Screen Quality Heatmap Visualization


Large datasets can be visualized using a heatmap, which gives you an overview of how your data looks like.
CRISPRAnalyzeR offers different heatmaps to visualize your data.

What Heatmap Visualizations can I use?

CRISPRAnalyzeR provides you different settings to adjust the heatmap visualization.

CRISPRAnalyzeR offers you six different types of heatmaps visualization. In addition, you can use K-Means clustering within selected heatmap types. Heatmaps allow you to look at your data in a structured way in order to check how your screen performed.

Gene Abundance
The gene abundance heatmap takes the read count of all sgRNAs for a given gene and calculates its fraction (in %) of the whole dataset.
Gene Read Count
With this type, the normalized read count for all sgRNAs of a given gene is summed up and visualized.
Gene Top/Low
Describes you how many sgRNAs for each gene where among the top or lowest 5 % of the dataset. This can be used to find potentially enriched or depleted genes.
sgRNA Abundance
The sgRNA abundance heatmap shows you the percentage of read counts wihtin the dataset accounted for each sgRNA.
sgRNA Read Count
This type describes the normalized read counts for each sgRNA in the dataset.

Did you know?

You can zoom into the heatmap to inspect interesting regions more closely. In addition, you can save an image of the current view using the upper right menu.



Step 1: Adjust the heatmap settings

You can adjust the heatmap using various parameters

You can download the plot via the upper right menu within the plot and add it to your CRISPRAnalyzeR Report by clicking on the Add Report button.

See Additional Information for a detailed explanation of all settings.
In case you selected Gene Top/Lowest 5%, you can set whether you want to see TOP (enriched) or LOWEST (depleted) genes.
This is useful if you have some outliers. With the logged color scale you will not only see those.
Depending on the library size, heatmap calculation can take some time.
Please be patient while CRISPRAnalyzeR generates it for you.

Get your heatmap

Heatmap

Hit Calling  Gene Ranking


CRISPRAnalyzeR performs data analysis using 8 different algorithms, which gives you a more sophisticated way to check for screening candidates. Take your time and have a look at each individual analysis method and compare them.

Which hit calling algorithms are implemented in CRISPRAnalyzeR?

CRISPRAnalyzeR offers you six different hit calling algorithm implementations to give you the chance to find the most robust hit within your screen. Since each algorithm performs the hit calling in a different way, we suggest you to have a look to all of them.

Before CRISPRAnalyzer applies hit calling, all data is normalized using DESeq2.

Wilcox

Analysis on sgRNA foldchange

The Wilcox implementation is based on a two-sided Mann-Whitney-U test, which compares the sgRNA population foldchange (between your treatment groups) of each gene to either the population of the non-targeting control (if specified) or randomly picked sgRNAs. Finally a P-value correction according to Benjamini-Hochberg is applied to correct for multiple testing.

MAGeCK

Analysis on sgRNA read counts

MAGeCK is a stand-alone algorithm to perform hit calling in pooled CRISPR screens. MAgeCK is based on a RRA ranking algorithm to identify hit candidates.

Li, et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology 15:554 (2014)

edgeR

Analysis on sgRNA read counts

In brief, all read count data is modelled using an overdispersed Poisson model. Gene dispersions are then estimated by conditional maximum likelihood and shrunk using an empirical Bayes procedure. Finally, differential expression is assessed using an adapted Fisher's exact test. EdgeR analysis has been implemented as previously published in

Dai,Z. et al. (2014) edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens. F1000Research, 3, 95.

DESeq2

Analysis on Gene-level read counts

The DESeq2 implementation is based on the DESeq2 R package. It uses the summed read counts of all sgRNAs for a given gene and tests for differential effects based on a negative binomial distribution model. In brief, eead counts for all sgRNAs are summed up to obtain the total read count per gene. DESeq2 analysis is performed on these read counts, which includes normalization, estimation of size-factors and variance stabilization using a parametric fit. A Wald test for difference in log2-foldchanges between both conditions is done to determine enrichment/depletion effects. For more information about DESeq2, please see the DESeq2 manual available at Bioconductor or the publication.

Love MI, Huber W and Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, pp. 550. doi: 10.1186/s13059-014-0550-8.

sgRSEA

Analysis on sgRNA read counts

sgRSEA is based on the sgRSEA R package (Enrichment Analysis of CRISPR/Cas9 Knockout Screen Data), which is available at the R CRAN page. in brief, sgRSEA is based on a single-guide RNA Set Enrichment Analysis. First, sgRNAs are ranked by a signal-to-noise ratio. Then, the distribution of sgRNA ranks of a sgRNA set is compared with the overall distribution using a so-called enrichment score, which is based on a one-sided Kolmogorov Smirnov statistic and reflects the degree to which a sgRNA set is overrepresented at the top/the bottom of the ranked list.

Z-Ratio

Analysis on sgRNA/Gene-level read counts

A Z-Ratio is calculated between your two treatment groups for each sample and was originally used for microarray data. The Z-Ratio has been implemented as previously published.

Analysis of Microarray Data Using Z Score Transformation Chris Cheadle, Marquis P. Vawter, William J. Freed, Kevin G. Becker J Mol Diagn. 2003 May; 5(2): 73–81. doi: 10.1016/S1525-1578(10)60455-2

What visualization does CRISPRAnalyzeR offer?

CRISPRAnalyzeR offers you three different visualizations for all hit calling methods that have been implemented.

  • Ranked P-Values by significance
  • Distribution of unadjusted P-Values
  • Log2-transformed foldchanges plotted against the -log10 transformation of the corresponding adjusted p-values


CRISPRAnalyzeR performs the analysis to find more abundant (enriched) or less abundant (depleted) genes between your treatment groups. Genes which have shown a significant change, according to the p-value thresholds you have specified, are highlighted in red color in all plots.


Hit Calling   Overview


This page gives you the overall view for each analysis method and allows you to do a compare-on-a-glance.
Venn diagrams illustrate a hit candidate overlap for those that showed up below the defined p-value threshold in the different algorithms.

How can I find overlapping hit candidates?

Select the analysis methods to see overlapping hit candidates

In case you select an analysis method using the check boxes below, CRISPRAnalyzeR includes its results to show overlapping hit candidates. Genes are only considered as overlapping candidates between the methods, if they are below the p-value threshold of each method that you have set in the analysis settings. If there are no overlapping genes, reduce the number of included analysis methods and check the individial hit calling candidates of each analysis method.

Gene List Table

You can select which analysis algorithms you would like to compare - and whether you would like to have a look at enriched or depleted hit candidates. Remember that enriched means the gene showed a positive fold change between your treated and untreated group, and depleted indicates the exact opposite (a negative fold change). You can select and de-select methods and by this, get the common hit candidates among them.

Venn Diagram

A Venn diagram provides you the information, how many genes overlap in the different analysis algorithms. The number within a shared area indicates the number of overlapping candidates between these methods. A detailed explanation on Venn diagrams can be found on Wikipedia.



Find signficant candidates across all methods


Exclude genes which did not show significant enrichment/depletion according to the checked methods.
significantly enriched/depleted genes across all methods

Hit Calling  Essential Genes


Compare your genes against the DAISY Core Essentials and known essential genes of various cell lines according to GenomeCRISPR.



DAISY Core Essentials

The DIASY core essential genes are derived from the publication by Traver Hart et. al
Hart,T. et al. (2015) Systematic discovery and classification of human cell line essential genes Cold Spring Harbor Labs Journals.



GenomeCRISPR Cell Line Essentials




Hit Confirmation Gene Overview


The Hit Confirmation section will provide you with more detailed information about your genes of interest.
This includes individual sgRNA performance, a genomic model, predicted genome binding sites, predicted sgRNA scores as well as a brief overview for each individual gene of your screen.

Once you select a gene, CRISPRAnalyzeR will gather all information for you

Why does CRISPRAnalyzeR reannotate sgRNAs?

CRISPRAnalyzeR reannotates every sgRNA within your screen using E-CRISP. With this information, CRISPRAnalyzeR can generate additional information and plots for, e.g. the genomic model or potential genomic binding sites.

Gene Information

General information about the selected gene are retrieved from Ensembl, EnrichR and KEGG.

Gene/sgRNA Model

CRISPRAnalyzeR creates a genomic view similar to a genome browser, which includes gene or sgRNA information.

Published Screens

You can check whether you gene of interest has been used in previously published CRISPR screens. Moreover, information about observed phenotypes is presented.

Gene Peformance

CRISPRAnalyzeR gives you a brief overview of how the selected gene performed in your screen compared to all other genes.

COSMIC Mutation Database

CRISPRAnalyzeR retrieves additional information about somatic cancer mutation from the Sange COSMIC database. Please visit the COSMIC website to find more information about the COSMIC database.

Gene Ontology

CRISPRAnalyzeR retrieves additional information from the the Gene Ontology Consortium. Please visit the Gene Ontology website to find more information about Gene Ontology.



Please select your gene of interest


After selecting a gene, CRISPRAnalyzeR will automatically start to retrieve all information.

Hit Confirmation   sgRNA Performance


It is nice to know about possible hit candidates, but how about the sgRNAs that were used to target these genes?
On this page you can further investigate the performance and properties of sgRNAs targeting the select gene.

The sgRNA section of the In-Depth Analysis allows you to have a closer look the performance and properties of individual sgRNAs targeting the selected gene.

Readcount

After you have selected a gene on the left, you can check the read counts for each sgRNA targeting the selected gene. Moreover, you can select to get the normalized or non-normalized, raw read counts plotted. Remember that you can zoom into the plot as well as select/deselect the samples by clicking on their names in the legend below the plot.

Log2 Foldchange

This shows the fold changes of individual sgRNAs between your treatment groups in a log2-transformed visualization. You can sort the sgRNAs according to the log2 fold change.

Genomic Binding Sites

CRISPRAnalyzeR uses the re-evaluation feature of E-CRISP.org to reannotate your sgRNAs. This includes the analysis of predicted genomic binding sites, for which each sgRNAs is checked for potential genomic binding sites with a maximum of two mismatches and ignoring the first nucleotide base.

Z-Score

The Z-Score or standard score is a z-transformation according to this calculation.

Efficiency Scores

E-CRISP.org also provides some external efficiency scores. For more information, please visit the E-CRISP website.

Seed GC %
The GC content in per cent of the 8 basepairs proximal to the PAM sequence
Doench
Efficacy scoring as introduced by Doench et al. 2014 Nat. Biotech.
XU
Efficacy scoring as introduced by Xu et al. 2015 Gen.Res.

E-CRISP Scores

CRISPRAnalyzeR provides you with the scores that E-CRISP.org calculates for each sgRNA. An overview of the presented scores can be found at the E-CRISP website.

Specificity
Specificity Score
Annotation
Annotation Score
Efficiency
E-CRISP Efficacy Score
CDS
Coding Sequence Score
Exon
Exon Targeting Score

sgRNA Sequence

Here you can find an overview of your sgRNA target sequences, the fold changes, the Z-Score and the number of predicted targets for each sgRNA. Don't forget that you can download the table using the buttons at the upper left of the table.

Predicted sgRNA Binding Sites

Similar to the genomic binding sites, the predicted sgRNA binding sites tells you if the sgRNA has predicted target within an annotated gene or between two genes (intergenic). In case a sgRNA targets the same target more than once, the predicted target is only listed once.



Investigate Gene

Select gene of interest.

Hit Confirmation  Interaction Networks and Gene Set Enrichment


CRISPRAnalyzeR offers you the enrichment of gene sets with interaction networks from the String Database as well as additional information from EnrichR and various other databases.

Can I use several genes?

Yes, you can select single or multiple genes to start gene set analysis.

How is the Gene Set Analysis performed?

Gene Set Analysis is carried out using the Enrichr API available at the Enrichr website.

CRISPRAnalyzeR presents you each analysis with the Enrichr Combined Score, which is a combination of both the p-value and z-score:

c = log(p) * z

If you want to learn more about Enrichr, you can also check out the following publication:

Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr:
interactive and collaborative HTML5 gene list enrichment analysis tool.
BMC Bioinformatics. 2013;128(14)

Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A.
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.
Nucleic Acids Research. 2016; gkw377.

Pathways

CRISPRAnalyzeR checks the following databases for pathway information:

WikiPathways 2016
Wiki Pathway Database.
KEGG 2016
KEGG Database.
Biocarta 2016
Biocarta Database.
Reactome 2016
Reactome Database.
NCI-Nature 2016
NCI Nature Database.
Panther 2016
Panther Database.

Transcription

Gene Set Analysis is performed using different databases for transcriptional information provided by the Enrichr service

ChEA 2015
Enrichr Download Page.
TRANSFAC and JASPAR PWMs
JASPAR Transcription Factor Binding Sites.
ENCODE and ChEA Consensus TFs from ChIP-X
Download Dataset from Enrichr.
TargetScan microRNA Targets
TargetScan Website.
Transcription Factor PPIs
Download Dataset from Enrichr.

Ontologies

CRISPRAnalyzer uses Enrichr to get Gene Ontology Gene set analysis from the Gene Ontology Consortium.

GO Biological Process 2015
Gene Ontology Website.
GO Cellular Component 2015
Gene Ontology Website.
GO Molecular Function 2015
Gene Ontology Website.

Diseases

CRISPRAnalyzeR asks Enrichr to retrieve data from the Online Mendelian Inheritance in Man® Database.

OMIM
Online Mendelian Inheritance in Man®.

Cell Types

Retrieve genetic information about cell lines.

Cancer Cell Line Encyclopedia
Cancer Cell Line Encyclopedia.
NCI-60 Cancer Cell Lines
BioGPS Portal.

Protein Interactions

You can get a protein interaction network using the String Database.

String Database
String Database.


Select Genes

Select individual genes or gene lists

Select individual genes for Gene Set Analysis

Select gene lists for Gene Set Analysis


You can add up to 200 genes for the analysis.


Hit Confirmation   Gene Comparison


It is nice to know about possible hit candidates, but how about the sgRNAs?
This page provides you with a more in-depth view on the sgRNAs which you used to target your favourite genes.

Compare the sgRNA populations of different genes using violin plots.

Find out more about violin plots at Wikipedia.

Readcount Untreated Group

This violin plots shows the normalized read counts of each sgRNA for the selected genes within the samples marked as the untreated group. The width of the violine gives you an impression of the sgRNA population density.

Readcount Treated Group

This violin plot shows the normalized read counts of all sgRNAs for the selected genes within the samples that have been set as part of the treated group.

Log2 Foldchange

Shows the log2-transformed fold change of all sgRNAs for the selected genes between your treated and untreated group.

Z-Score

Shows the z-score of sgRNAs for the selected genes.

sgRNA Binding Sites

Displays the number of predicted genomic binding sites of all sgRNAs for the selected genes. Genomic binding sites are predicted using E-CRISP.org with ignoring the first nucleotide and allowing up to two mismatches in total.



Compare Genes

Select the genes you wish to compare.

Hit Confirmation   Gene Annotation


The more information, especially about annotated fateures, you get about possible hit candidates - the better.
You can get annotation for multiple genes and compare these.

Select multiple genes as a variety of external information to annotate your genes of interest.

Selecting Genes

You can select multiple genes for which you would like to retrieve additional information. Just start typing the gene identifier and CRISPRAnalyzeR will show you the matching genes of your screen.

How many Annotations can I add?

CRISPRAnalyzeR allows you to enter up to 20 annotation features. Just start typing and CRISPRAnalzyeR will tell you which annotations match your input.

Which Annotations are available?

CRISPRAnalyzeR offers you all annotations available via the Ensembl biomaRt service

just start typing what you would like to know and CRISPRAnalyzeR will show you mathcing annotations directly. Moreover you will find a list of all available annotations below.



Which Genes do you wish to annotate?

Select (multiple) genes of interest.

Available Annotations

Please select up to 20 different annotation features.
The input supports instant search, so just type and find interesting annotations.

This may take a few seconds.

Download Report Adjust your report


CRISPRAnalyzeR not only provides you with individually plots and tables, but also gives you the opportunity to configure a report.

What is included in the report?

CRISPRAnalyzeR automatically includes all plots and data of the screen quality section and the hit calling section. For all additional information, you can specifically add those information by clicking on the 'Add to Report' button to tell CRISPRAnalyzeR that you would like to have this included in the report.

Tell CRISPRAnalyzeR which additional information should be added to the report

You can select which information is included in the report

The download page will tell you which information was be added to the report. Here you also have the chance to include or not include complete sections, which can be usefull if you would like to generate a report that only contains specific parts of the analysis.

Can I add additional information to the report?

In step2, you can add additional screening information and comments to the report, such as a screening title, the screening workflow procedure, a hypothesis, the used cell line and many more. We advise you to include as much information to have a complete documentation of the screen.

How can I download my report?

The report is generated once you click on the 'Create HTML Report' button at the bottom of the page. The report generation will take some time, dependend on the amount of included data and plots - please be patient. In the meantime you can use CRISPRAnalyzeR, as the report is generated in the background and will be downloaded to your device automatically.




You can get a full report including all data, which can be adjusted and downloaded on this site.

Once you created plots and tables on this website, you can also download them as part of an interactive HTML file. Thereby, you not only have them organized in a single document but they are also still interactive, which is good for browsing and exploring the data offline. If you really like our plots and want to use them for a presentation or print them, you can always click on the download icon in the top right corner of each plot - this also works in the downloaded report. By doing this, you can convert a plot to a .pdf, .png or other formats.


Step 1: Select Components that will be present in the report

In general, CRISPRAnalyzeR will add all common plots and tables to the report.
For all plots and tables that depent on your direct selection, you have to add these to the report by clicking a button. Throughout CRISPRAnalyzeR you will find the 'Add to Report'-buttons, which will add the generated plots/tables to your report.

Please note: the CRISPRAnalyzeR report is a fully-interactive HTML file
Please select the components to be included in your report:
Include plots and tables that represent your screen in various ways.
Include plots and tables of different differential expression analyses employed on your screen.

Step 2: Provide general information about your screen

A good reports needs additional information about the screening procedure.

Experiment Title

Aim / Hypothesis of the screen


Screening Procedure


Organism


Cell Line


Experimentator


Plasmid used


sgRNA Library Name


Number of Cells per sgRNA (Coverage)


Treatment


Sequencing Primer


Sequencing


Additional Comments

Please add further information in the text boxes above so CRISPRAnalyzeR can include them into your report.
Download HTML Report
Depending on the size of your screen the report generation might take several minutes up to an hour.
Please be patient - the report will be ready for download as soon as it is finished.


For genome-wide screens, the report generation might fail due to memory limitations.
You can find a solution at the Github page
.

Help   Available Tutorials


Below you find tutorials on how to use CRISPRAnalyzeR or for specific settings you can adjust.

Available Video Tutorials

All tutorials can also be found on Youtube

CRISPRAnalyzeR Usage


How to upload FASTQ.gz data


How to find the right regular expression for FASTQ data


How to upload read count data


How to set the analysis parameters

Not available yet.

How to assess the sequencing and screening quality


How to upload read count data and set the analysis parameters


How to find candidate genes in the Hit Calling section

Not available yet.

How to annotate genes in the Hit Confirmation section

Not available yet.

How to compare effects among multiple genes

Not available yet.

How to set up and generate the interactive report

Not available yet.


Installation


How to setup and install CRISPRAnalyzeR on macOS



Help   Google Forum


Our main goal was to keep CRISPRAnalyzeR easy-to-use and straight-forward.
But - to be honest - with each new user things might come up that might not be clear to the user or just don't work as intended.
We apologize for any inconvenience and ask you to please let us know what struggled you.

To provide you a fast and convenient way of asking questions, please join our Google Forum below.

Please have a look into the google groups of CRISPRAnalyzer for additional help. Check out the CRISPRAnalyzeR google group.


About CRISPRAnalyzeR  About us / Imprint



This application is a non-commercial project, which is funded and hosted by the German Cancer Research Center (dkfz).
It is developed by Jan Winter and Marc Schwering.

Jan Winter

PhD Student

Division of Signaling and Functional Genomics

jan.winter@dkfz-heidelberg.de

LinkedIn Twitter
Marc Schwering

Master Student

Division of Signaling and Functional Genomics

m.schwering@dkfz-heidelberg.de

LinkedIn Twitter

CRISPRAnalyzeR does not store personal information. All uploaded data is deleted immediately once the browser is closed or the connection is lost for any reason.


Impressum / Imprint (German Only)

Angaben gemäß § 5 TMG:

Deutsches Krebsforschungszentrum (dkfz)
Im Neuenheimer Feld 280
69120 Heidelberg


Vertreten durch:

Prof. Dr. Michael Baumann (wissenschaftlicher Vorstand)
Prof. Dr. Josef Puchta (Kaufmännischer Vorstand)


Kontakt:

Telefon:
06221421953
Telefax:
06221421959
Email:
jan.winter@dkfz-heidelberg.de


Umsatzsteuer-Identifikationsnummer gemäß §27 a Umsatzsteuergesetz

DE 143293537


What data is stored?

CRISPRAnalyzeR does not store personal information. All uploaded data is deleted immediately once the browser is closed or the connection is lost for any reason.

CRISPRAnalyzeR only stores uploaded data for the time you visit the website.
Moreover, CRISPRAnalyzeR logs the following information anonymously for maintenance purposes:

Occuring error messages, file names, selected gene identifier, number of uploaded files, submit button activity and timestamps


CRISPRAnalyzeR uses the following technologies

Tools

Version
Bowtie2 2.29
Highcharts 5.04
MAGeCK 0.53
R 3.32

R Packages

Download CRISPRAnalyzeR  Download the source code or a Docker container





Download log files for debugging




Download CRISPRAnalyzeR Source Code


Download CRISPRAnalyzeR as ready-to-use Docker Container