bcSeq-package: Fast Sequence Alignment for High-throughput shRNA and CRISPR...
In bcSeq: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens

Description Details Author(s) See Also Examples

This Rcpp-based package implements highly efficient data structure and algorithms for performing the alignment of short 'CRISPR' or shRNA screens reads to library barcodes based on user specified mismatch, insert and delete tolerance. Matching qualities are then evaluated based on Phred score. A Bayers' classifier is employed to determine the originating barcode of a read. We support user-defined probability model for evaluting matching qualities as well as flexible output.The alignment also support multiple-thread to reduce the processing time in the C++ implementation.

Package:	bcSeq
Type:	Package
Version:	1.5.9
Date:	2019-04-23
License:	GPL-3

Please see the example function calls below, or refer to the individual function documentation or the included vignette for more information. The package vignette serves as a tutorial for using this package.

Jiaxing Lin, Jeremy Gresham, Tongrong Wang, So Young Kim, James Alvarez, Jeffrey S. Damrauer, Scott Floyd, Joshua Granek, Andrew Allen, Cliburn Chan, Jichun Xie, Kouros Owzar

Maintainer: Jiaxing Lin <jiaxing.lin@duke.edu>

Rcpp

#### Generate barcodes
lFName    <- "./libFile.fasta"
bases     <- c(rep('A', 4), rep('C',4), rep('G',4), rep('T',4))
numOfBars <- 15
Barcodes  <- rep(NA, numOfBars*2)
for (i in 1:numOfBars){
    Barcodes[2*i-1] <- paste0(">barcode_ID: ", i)
    Barcodes[2*i]   <- paste(sample(bases, length(bases)), collapse = '')
}
write(Barcodes, lFName)

#### Generate reads and phred score
rFName     <- "./readFile.fastq"
numOfReads <- 100
Reads      <- rep(NA, numOfReads*4)
for (i in 1:numOfReads){
    Reads[4*i-3] <- paste0("@read_ID_",i)
    Reads[4*i-2] <- Barcodes[2*sample(1:numOfBars,1,
        replace=TRUE, prob=seq(1:numOfBars))]
    Reads[4*i-1] <- "+"
    Reads[4*i]   <- paste(rawToChar(as.raw(
        33+sample(20:30, length(bases),replace=TRUE))),
        collapse='')
}
write(Reads, rFName)

#### perform alignment
outFile  <- "./counthamming.csv"
#res <- bcSeq_hamming(rFName, lFName, outFile, misMatch = 2,
#    tMat = NULL, numThread = 4, count_only = TRUE)
outFile  <- "./countedit.csv"
#res <- bcSeq_edit(rFName, lFName, outFile, misMatch = 2,
#    tMat = NULL, numThread = 4, count_only = TRUE,
#    gap_left = 2, ext_left = 1, gap_right = 2, ext_right = 1,
#    pen_max = 7, userProb = NULL)