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It is also possible to either enrich the screening dataset file with additional information provided by the biomaRt interface. For example, gene identifiers can be changed from EnsemblIDs to official gene symbols are Gene Ontology terms can be added to the dataset. This can be done using 'get.gene.info', which serves as a wrapper for the **biomaRt** package with its load of options and possibilities (more information see '?biomaRt').
● Data Source:
CranContrib
● Keywords: ~Analysis
● Alias: get.gene.info
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CaRpools offers two ways of providing CRISPR/Cas9 screening data. Either raw **read-count files** are directly used as described before, or read-count files are generated from NGS FASTQ files by extracting the 20 nt target sequence, mapping it against a reference library and extracting the read-count information for each sgRNA identifier.
● Data Source:
CranContrib
● Keywords: ~NGS
● Alias: data.extract
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You can verify that the MIACCS.xls file as well as the used template file and all necessary scripts are found by calling 'check.caRpools()'. CaRpools also uses MAGeCK to look for enriched or depleted genes within your screening data. Please note that MAGeCK needs to be installed correctly, this can be tested by 'check.caRpools'.
● Data Source:
CranContrib
● Keywords: ~Analysis
● Alias: check.caRpools
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Aggregate all sgRNA data from pooled CRISPR screens to their corresponding gene level.
● Data Source:
CranContrib
● Keywords: CRISPR
● Alias: aggregatetogenes
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__Wilcox__
● Data Source:
CranContrib
● Keywords: ~Analysis
● Alias: stat.wilcox
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Analysis of pooled CRISPR screens based on mapped NGS readcount data or raw NGS FASTQ file.
● Data Source:
CranContrib
● Keywords: Analysis, CRISPR, package
● Alias: CRISPR-AnalyzeR, CRISPR-AnalyzeR-package
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Since in most cases several sgRNAs are used to target a gene, the information how many sgRNAs are present in the data for each gene is of interest to make sure the number of sgRNAs present is still sufficient. Typically, only few sgRNAs should get "lost" during the screening procedure, so that the full sgRNA coverage is maintained throughout the assay. The only exception would be drop-out screens with a stringent setup. The representation of sgRNAs per gene can be plotted using 'carpools.reads.genedesigns'. For further details see '?carpools.reads.genedesigns'.
● Data Source:
CranContrib
● Keywords: ~coverage, ~sgRNA
● Alias: carpools.reads.genedesigns
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Moreover, caRpools report generation can also be initiated without R-studio installation, so that this can be done via R command line even on remote computers. In this case, caRpools report generation can be started via 'use.caRpools' with additional parameters, which are described below.
● Data Source:
CranContrib
● Keywords: ~Report
● Alias: use.caRpools
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CaRpools also uses MAGeCK to look for enriched or depleted genes within your screening data. Please note that MAGeCK needs to be installed correctly, this can be tested by 'check.caRpools'.
● Data Source:
CranContrib
● Keywords: ~Analysis
● Alias: stat.mageck
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General statistics for a given dataset can be obtained by 'stats.data'.
● Data Source:
CranContrib
● Keywords: ~Analysis
● Alias: stats.data
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providing 
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