# Automated analysis of CRISPR experiments

## Documentation for package ‘amplican’ version 1.2.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

amplican-package | Automated analysis of CRISPR experiments.
amplican | Automated analysis of CRISPR experiments.
amplicanAlign | Align reads to amplicons.
amplicanConsensus | Extract consensus out of forward and reverse events.
amplicanFilter | Filter Events Overlapping Primers, PRIMER DIMERS and Low Alignment Score Events.
amplicanMap | Map events to their respective relative coordinates specified with UPPER case.
amplicanNormalize | Remove events that can be found in Controls.
amplicanOverlap | Check which events overlap expected cut sites.
amplicanPipeline | Wraps main package functionality into one function.
amplicanReport | Prepare reports as .Rmd files.
amplicanSummarize | Summarize how many reads have frameshift and how many reads have deletions.
amplican_print_reads | Pretty print forward and reverse reads aligned to each other.
cigarsToEvents | Transform extended CIGAR strings into GRanges.
comb_along | Generate all combinations along string exchanging m characters at a time with dictionary letters.
findEOP | Find Events Overlapping Primers.
findLQR | Find Off-targets and Fragmented alignments from reads.
findPD | Find PRIMER DIMER reads.
getEvents | Transform aligned strings into GRanges representation of events.
metaplot_deletions | MetaPlots deletions using ggplot2.
metaplot_insertions | MetaPlots insertions using ggplot2.
metaplot_mismatches | MetaPlots mismatches using ggplot2.
pairToEvents | Read "pair" format of EMBOSS needle into GRanges as events.
plot_cuts | Plots cuts using ggplot2.
plot_deletions | Plots deletions using ggplot2.
plot_height | Get figure height in inches for number of elements on y axis.
plot_heterogeneity | Plots heterogeneity of the reads using ggplot2.
plot_insertions | Plots insertions using ggplot2.
plot_mismatches | Plots mismatches using ggplot2.
plot_variants | Plots most frequent variants using ggplot2.