Welcome to the amplican package. This vignette will walk you through our main package usage with example MiSeq dataset. You will learn how to interpret results, create summary reports and plot deletions, insertions and mutations with our functions. This package, amplican, is created for fast and precise analysis of CRISPR experiments.

1 Introduction

amplican creates reports of deletions, insertions, frameshifts, cut rates and other metrics in knitable to HTML style. amplican uses many Bioconductor and CRAN packages, and is high level package with purpose to align your fastq samples and automate analysis across different experiments. amplican maintains elasticity through configuration file, which, with your fastq samples are the only requirements.

For those inpatient of you, who want to see an example of our whole pipeline analysis on attached example data look here. Below you will find the conceptual map of amplican.

Conceptual map of amplican.

Conceptual map of amplican.

Below you will find the amplicanConsensus rules. That allow you to understand how ampliCan treats unambiguous forward and reverse reads. Green color indicates events that will be accepted. When forward and reverse reads agree, their events are in the same place and span the same length, we will take forward read event as representative. In case when events from forward and reverse read don’t agree we select event from strand with higher alignment score. In situation where one of the reads is not spanning event in question we consider this event as real (as we don’t have other information). If both strands cover event in question, but one strand has no indel, amplicanConsensus will change behavior according to the strict parameter.

Consensus rules of ampliCan.

Consensus rules of ampliCan.

2 Configuration file

To successfully run our analysis it is mandatory to have a configuration file. Take a look at our example:

ID Barcode Forward_Reads Reverse_Reads Group Control guideRNA Forward_Primer Reverse_Primer Direction Amplicon Donor
ID_1 barcode_1 R1_001.fastq R2_001.fastq Betty 0 AGGTGGTCAGGGAACTGG AAGCTGACGGCTAAATGA AATTACACAAGCGCAAACACAC 0 aagctgacggctaaatgaaaaatgtcaaacatctgttccaggtgctgcgtatgccagggcagaggAGGTGGTCAGGGAACTGGtggaggtcactgggataccctttcttcccacaccaatggggaaaggagtcctgccagatgaccatcccaactgtgttgctgcagccagatccaggtgtgtttgcgcttgtgtaatt
ID_2 barcode_1 R1_001.fastq R2_001.fastq Tom 0 TGACCCTCTGCCAACACAAGGGG TGACCAAACCTTCTTAAGGTGC CTCTGCTGCAAAATGCAAGG 1 aaatactgtcttgtgaccaaaccttcttaaggtgctattttgataataaactttattgtgcttttgtagttgtgCCCCTTGTGTTGGCAGAGGGTCAgcagaccagtaagtcttctcaatttcttttatttatgtgtagtgataaaaaaatgttaaattaaaattaaatgtttttttttgccttgcattttgcagcagaggatgat
ID_3 barcode_2 R1_002.fastq R2_002.fastq Tom 0 AGGTGGTCAGGGAACTGG AAGCTGACGGCTAAATGA AATTACACAAGCGCAAACACAC 0 aagctgacggctaaatgaaaaatgtcaaacatctgttccaggtgctgcgtatgccagggcagaggAGGTGGTCAGGGAACTGGtggaggtcactgggataccctttcttcccacaccaatggggaaaggagtcctgccagatgaccatcccaactgtgttgctgcagccagatccaggtgtgtttgcgcttgtgtaatt
ID_4 barcode_2 R1_002.fastq R2_002.fastq Betty 0 GTCCCTGCAACATTAAAGGCCGG GCTGGCAACATTCCTACCAGT GAGCGCTGAGGCAGGATTAT 0 gctggcaacattcctaccagtaatttacgtaaaaaaatgctataaaatgtgtagctctccagtctaatgtaacttgtgcttgcattgtgtttacaggaaaccaGTCCCTGCAACATTAAAGGCCGGaagtctaagaactcacatcagcaggtgtcaagtgtgcatgaagagggtataatcctgcctcagcgctc
ID_5 barcode_2 R1_002.fastq R2_002.fastq Betty 0 GTCCCTGCAACATTAAAGGCCGG ACTGGCAACATTCCTACCAGT ACTGGCTGAGGCAGGATTAT 0 actggcaacattcctaccagtaatttacgtaaaaaaatgctataaaatgtgtagctctccagtctaatgtaacttgtgcttgcattgtgtttacaggaaaccaGTCCCTGCAACATTAAAGGCCGGaagtctaagaactcacatcagcaggtgtcaagtgtgcatgaagagggtataatcctgcctcagccagt actggcaacattcctaccagtaatttacgtaaaaaaatgctataaaatgtgtagctctccagtctaatgtaacttgtgcttgcattgtgtttacaggaaaccaGTCCCTGCAACATTAAAAGCCGGaagtctaagaactcacatcagcaggtgtcaagtgtgcatgaagagggtataatcctgcctcagccagt

Configuration file should be a “,” delimited csv file with information about your experiments. You can find example config file path by running:

system.file("extdata", "config.csv", package = "amplican")

Columns of the config file:

If you have only forward primers leave column Reverse_Primer empty, leave empty also the Reverse_Reads column. You can still use amplican like normal.

3 Default options

To run amplican with default options, along with generation of all posible reports you can use amplicanPipeline function. We have already attached results of the default amplican analysis (look at other vignettes) of the example dataset, but take a look below at how you can do that yourself. Be prepared to grab a coffe when running amplicanPipeline with knit_files = TRUE as this will take some time. You will understand it is worth waiting when reports will be ready.

# path to example config file
config <- system.file("extdata", "config.csv", package = "amplican")
# path to example fastq files
fastq_folder <- system.file("extdata", package = "amplican") 
# output folder, a full path
results_folder <- tempdir()  

#  run amplican
amplicanPipeline(config, fastq_folder, results_folder)

# results of the analysis can be found at

Take a look into “results_folder” folder. Here you can find .Rmd files that are our reports for example dataset. We already crafted .html versions and you can find them in the “reports” folder. Open one of the reports with your favourite browser now. To zoom on an image just open it in new window (right click -> open image in new tab).

amplicanPipeline just crafted very detailed reports for you, but this is not all, if you need something different e.g. different plot colours, just change the .Rmd file and knit it again. This way you have all the power over plotting.

4 Files created during analysis

4.1 barcode_reads_filters.csv

First step of our analysis is to filter out reads that are not complying with our default restrictions:

  • bad base quality - default minimum base quality is 0
  • bad average quality - default minimum average quality is 30
  • bad alphabet - by default we accept only reads with A,C,T,G bases
Barcode experiment_count read_count bad_base_quality bad_average_quality bad_alphabet filtered_read_count unique_reads unassigned_reads assigned_reads
barcode_1 2 20 0 3 3 14 8 1 7
barcode_2 3 21 0 0 0 21 9 0 9

This table is also summarized in one of the reports. As you can see for our example dataset we have two barcodes, to which correspond 21 and 20 reads. Six reads are rejected for barcode_1 due to bad alphabet and bad average quality. Each of the barcodes has unique reads, which means forward and reverse reads are compacted when they are identical. There is 8 and 9 unique reads for each barcode. One read failed with assignment for barcode_1, you can see this read in the top unassgned reads for barcode report in human readable form. Normally you will probably see only half of your reads being assigned to the barcodes. Reads are assigned when for forward read we can find forward primer and for reverse read we can find reverse primer. Primers have to be perfectly matched. Nevertheless, there is option fastqreads = 0.5 which changes method of assigning reads to each IDs. With this option specified only one of the reads (forward or reverse) have to have primer perfectly matched. If you don’t have the reverse reads or you don’t want to use them you can use option fastqreads = 1, this option should be detectd autmatically, if you leave empty field Reverse_Primer in the config file.

4.2 config_summary.csv

config_summary.csv contains extended version of the config file. It should provide you additional look at raw numbers which we use for various plots in reports. Take a look at example extension:

ID Barcode Reads Reads_Filtered Reads_In Reads_Del Reads_Edited Reads_Frameshifted
ID_1 barcode_1 7 6 0 6 6 2
ID_2 barcode_1 6 6 0 6 6 4
ID_3 barcode_2 9 8 1 7 8 1
ID_4 barcode_2 7 7 7 4 7 2
ID_5 barcode_2 5 5 0 0 2 0

During amplicanPipeline these columns are added to the config file:

  • Reads_Del, Reads_In, Reads_Edited - number of deletions, insertions or any of those two (mutations) overlapping with user specified UPPER CASE group in the amplicon (extended by the buffer), events are confirmed with the reverse strand when using paired-end reads, for more information check amplicanConsensus
  • Frameshifted - number of reads that have frameshift (insertions and deletions)
  • PRIMER_DIMER - number of reads that were classified as PRIMER DIMERs
  • Reads - number of reads assigned to this unique ID
  • Reads_Filtered - number of reads assigned to this unique ID with excusion of PRIMER DIMERs and Low Alignment Score reads

4.3 RunParameters.txt

File RunParameters.txt lists all options used for the analysis, this file you might find useful when reviewing analysis from the past where you forgot what kind of options you used. Other than that this file has no purpose.

# path to example RunParameters.txt
run_params <- system.file("extdata", "results", "RunParameters.txt", 
                          package = "amplican")
# show contents of the file
##  [1] "Config file:         full/path/to/config/file/that/has/been/used.csv"
##  [2] "Average Quality:     30"                                             
##  [3] "Minimum Quality:     0"                                              
##  [4] "Write Alignments:    txt"                                            
##  [5] "Fastq files Mode:    0"                                              
##  [6] "Gap Opening:         25"                                             
##  [7] "Gap Extension:       0"                                              
##  [8] "Consensus:           TRUE"                                           
##  [9] "Normalize:           guideRNA, Group"                                
## [10] "PRIMER DIMER buffer: 30"                                             
## [11] "Cut buffer: 5"                                                       
## [12] "Scoring Matrix:"                                                     
## [13] ",A,C,G,T"                                                            
## [14] "A,5,-4,-4,-4"                                                        
## [15] "C,-4,5,-4,-4"                                                        
## [16] "G,-4,-4,5,-4"                                                        
## [17] "T,-4,-4,-4,5"

4.4 “alignments” folder

As name indicates it contains all alignments.

# path to the example alignments folder
system.file("extdata", "results", "alignments", package = "amplican")

In unassigned_reads.csv you can find detailed information about unassigned reads. In example dataset there is one unassigned read.

Take a look at the alignment events file which contains all the insertions, deletions, cuts and mutations. This file can be used in various ways. Examples you can find in .Rmd files we prepare using amplicanReport. These can be easily converted into GRanges and used for further analysis! Events are saved at three points of amplicanPipeline analysis. First file “raw_events.csv” contains all events directly extracted from aligned reads. After filtering PRIMER DIMER reads, removing events overlapping primers (alignment artifacts) and shifting events so that they are relative to the expected cut sites “events_filtered_shifted.csv” is saved. After normalization through amplicanNormalize “events_filtered_shifted_normalized.csv” is saved, probably it is the file you should use for further analysis.

seqnames start end width strand originally replacement type read_id score counts readType overlaps consensus
ID_1 -24 41 66 + deletion 1 597 3 FALSE TRUE TRUE
ID_1 -28 45 74 + deletion 2 557 2 FALSE TRUE TRUE
ID_1 -32 51 84 + deletion 4 532 1 FALSE TRUE TRUE
ID_1 -35 -35 1 + A G mismatch 1 597 3 FALSE FALSE TRUE

Human readable alignments can be accesed using lookupAlignment function of AlignmentsExp