caRpools (version 0.83)

data.extract: Extracting sgRNA information from NGS FASTQ files to create read-count files for caRpools Analysis

Description

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.

In a first step, NGS FASTQ data is extracted and mapped against a reference library file using bowtie2.

Usage

data.extract(scriptpath=NULL, datapath=NULL, fastqfile=NULL, extract = FALSE,
pattern = "default", machinepattern = "default", createindex = FALSE,
referencefile = NULL, mapping = FALSE, reversecomplement = FALSE,
threads = 1, bowtieparams = "", sensitivity = "very-sensitive-local", match = "perfect")

Arguments

scriptpath

Absolute path of the folder that contains `CRISPR-extract.pl` and `CRISPR-mapping.pl` *Default* NULL *Values* absolute path (character)

datapath

Absolute path of the folder that contains the data files (e.g. file.FASTQ) *Default* NULL *Values* absolute path (character)

fastqfile

Filename of FASTQ file WITHOUT .fastq extension *Default* NULL *Values* filename (character)

extract

Whether CRISPR-extract.pl is used to extract the 20 nt target sequence from the NGS reads using `pattern` *Default* FALSE *Values* TRUE, FALSE (boolean)

pattern

PERL regular Expression to extract 20 nt target sequence from NGS reads. Please see *extract pattern* in this manual for more information. *Default* Regular Expression (character)

machinepattern

Maschine ID of your Sequencing maschine. Used ot identify the read id.

createindex

Do you want caRpools to generate a bowtie2 index? Only necessary if `mapping=TRUE`. *Default* FALSE *Values* TRUE, FALSE

referencefile

Filename of the library reference FASTA file, without extension. Is the same as bowtie2 file, if `createindex=TRUE`.

mapping

Indicates whether FASTQ files need to be mapped against `referencefile`/`bowtie2file`. FALSE by default. *Default* FALSE *Values* TRUE, FALSE

reversecomplement

Is the NGS sequence in reverse complement order? *Default* FALSE *Values* TRUE, FALSE

threads

How many threads can bowtie2 use for mapping? Only used if `mapping=TRUE`. Usually cores of CPU. *Default* 2 *Values* any integer

bowtieparams

If you want to pass additional parameters to bowtie2.

sensitivity

You can djust the sensitivity of bowtie2 using this parameter. By default, bowtie2 is used in a very-sensitive-local setting. More information about different sensitivy parameters can be found at the [bowtie2 options](http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml#options). *Default* "very-sensitive-local" *Other options: very-fast, fast, sensitive, very-fast-local, fast-local, sensitive-local*

match

After bowtie2 mapping, the aligment is converted into read count files *filename_extracted-design.txt* and *filename_extracted-genes.txt*. You can indiciate how well the alignment must be in order to be used for generating the read count for each sgRNA. By default, this is set to *perfect*, which only employs a mapped read if the full 20 nt from the sequencing match perfectly to the sgRNA found in your library reference. The following options can be used:

* __perfect__ - Read is used of all 20 nt from the sequencing are matching the target sequence given in the library reference * __high__ - Read is used if at least 18 nt (starting from the PAM) are matching the target sequence in the reference * __seed__ - Read is used if at least 14 nt (starting from the PAM) are a perfect match against the target sequence in the reference

Value

Returns file name for load.file(). Generated additional read-count files.

Details

none

Examples

data(caRpools)
# fileCONTROL1 = data.extract(scriptpath="path.to.scripts",
# datapath="path.to.FASTQ", fastqfile="filename1", extract=TRUE,
# seq.pattern, maschine.pattern, createindex=TRUE,
# bowtie2file=filename.lib.reference, referencefile="filename.lib.reference", 
# mapping=TRUE, reversecomplement=FALSE, threads, bowtieparams,
#sensitivity="very-sensitive-local",match="perfect")  
# Now we can load the generated Read-Count file directly!
#CONTROL1 = load.file(paste(datapath, fileCONTROL1, sep="/")) # Untreated sample 1 loaded

# Don't forget the library reference
# libFILE = load.file( paste(datapath, paste(referencefile,".fasta",sep=""), sep="/"),
# header = FALSE, type="fastalib")