Here we provide the tools to perform paired end or single read BreakTag raw data processing. The pipeline is also valid for (s)BLISS data. As input files you may use either gzipped fastq-files (.fastq.gz) or mapped read data (.bam files). In case of paired end reads, corresponding fastq files should be named using .R1.fastq.gz and .R2.fastq.gz suffixes. Some steps of the pipeline are based on the blissNP pipeline developed at the BriCo lab for BLISS data.
Initial preprocessing is typically done in a linux cluster using the Breaktag pipeline. It includes the following steps:
- scanning for reads (single- or paired-end) containing the expected 8-nt UMI followed by the 8-nt sample barcode in the 5' end of read 1.
- alignment of reads to reference genome with BWA, with a seed length of 19 and default scoring/penalty values for mismatches, gaps and read clipping.
- reads mapped with a minimum quality score Q (defaults to Q=60) are retained.
- close spatial consecutive reads within a window of 30 nucleotides and UMI differing with up to 2 mismatches are considered PCR duplicates and only one is kept.
The resulting reads are aggregated per position and reported as a BED file.
This R package implements the identification of CRISPR (currently, Cas9 only) targets and estimation of the scission profile. Additionally it provides several plotting functions to graphically summarize the results.
This repository contains a Dockerfile which can be used to create a Docker image with all dependencies.
This is the preferred and easiest way to have all the dependencies satisfied and run the pipeline.
Install and make available in the path the following dependencies:
- Bpipe
- Bedtools
- BWA
- FastQC
- MultiQC
- Samtools
- Several unix standard tools (perl5, python3, awk, etc.)
If you're running the pipeline in a cluster, you probably want to edit the tools config file and tell the pipeline how these tools are loaded (added in $PATH).
Tools are expected to be in the PATH. From the root of the folder where you clone the breaktag pipeline:
- edit the parameters file
breaktag/pipelines/breaktag/essential.vars.groovy - edit the targets file
breaktag/pipelines/breaktag/targets.txt - softlink these 2 files to the root folder
ln -s breaktag/pipelines/breaktag/essential.vars.groovy . && ln -s breaktag/pipelines/breaktag/targets.txt . - run the pipeline with this command (eg. from within the docker container):
bpipe run -n256 breaktag/pipelines/breaktag/breaktag.pipeline.groovy rawdata/*.fastq.gz-
targets.txt: tab-separated txt-file giving information about the analysed samples. The following columns are required- name: sample name. Experiment ID found in fastq filename: expID_R1.fastq.gz
- pattern: UMI+barcode pattern file used in the linker
-
essential.vars.groovy: essential parameters describing the experiment
- ESSENTIAL_PROJECT: root folder of the analysis
- ESSENTIAL_SAMPLE_PREFIX: sample name prefix to be trimmed in the results
- ESSENTIAL_THREADS: number of threads for parallel tasks
- ESSENTIAL_BWA_REF: path to bwa indexed reference genome
- ESSENTIAL_PAIRED: either paired end ("yes") or single read ("no") design
- ESSENTIAL_QUALITY: minimum mapping quality desired
Important parameters are included in this file. They're distributed in several sections.
ESSENTIAL_PROJECT="/project/folder"
ESSENTIAL_SAMPLE_PREFIX=""
ESSENTIAL_THREADS=16ESSENTIAL_BWA_REF="/ref/index/bwa/hg38.fa" // BWA index of the reference genome
ESSENTIAL_PAIRED="yes" // paired end design
ESSENTIAL_QUALITY=60 // min mapping quality of reads to be kept. Defaults to 60 (discarding multimappers and low quality mapping)You probably don't need to touch these lines:
// further optional pipeline stages to include
RUN_IN_PAIRED_END_MODE=(ESSENTIAL_PAIRED == "yes")
// project folders
PROJECT=ESSENTIAL_PROJECT
LOGS=PROJECT + "/logs"
MAPPED=PROJECT + "/mapped"
QC=PROJECT + "/qc"
RAWDATA=PROJECT + "/rawdata"
REPORTS=PROJECT + "/reports"
RESULTS=PROJECT + "/results"
TMP=PROJECT + "/tmp"
TRACKS=PROJECT + "/tracks"
TARGETS=PROJECT + "/targets.txt"More fine-grained tunning of the tools called by the pipeline can be controled from the .header files in the breaktag/modules folder.
A tab-separated file with the filenames (excluding the .fastq.gz extension) and the breaktag barcode and the position of the UMI within the read.
name pattern umi
FANCF_rep1 ^(........)(.TCACACGT|C.CACACGT|CT.ACACGT|CTC.CACGT|CTCA.ACGT|CTCAC.CGT|CTCACA.GT|CTCACAC.T|CTCACACG.) $1
FANCF_rep2 ^(........)(.TCACACGT|C.CACACGT|CT.ACACGT|CTC.CACGT|CTCA.ACGT|CTCAC.CGT|CTCACA.GT|CTCACAC.T|CTCACACG.) $1
NT_rep1 ^(........)(.TCACACGT|C.CACACGT|CT.ACACGT|CTC.CACGT|CTCA.ACGT|CTCAC.CGT|CTCACA.GT|CTCACAC.T|CTCACACG.) $1
NT_rep2 ^(........)(.TCACACGT|C.CACACGT|CT.ACACGT|CTC.CACGT|CTCA.ACGT|CTCAC.CGT|CTCACA.GT|CTCACAC.T|CTCACACG.) $1For your set of gRNAs, you may want to run the prediction of blunt rates of Streptococcus pyogenes Cas9 (SpCas9) using the XGBoost model trained with HiPlex1 data.
The main dependency is H2O.
Remove any previously installed H2O packages for R.
if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }
if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }Download packages that H2O depends on.
install.packages(c("RCurl","jsonlite", "devtools"))Download and install the H2O package for R. The models were trained on H2O version 3.36.1.2, therefore specifically install this version.
install.packages("h2o", type="source", repos="http://h2o-release.s3.amazonaws.com/h2o/rel-zumbo/2/R")Test the H2O installation with:
library(h2o)
localH2O = h2o.init()
demo(h2o.kmeans)Now, it's all set to install the package.
The package resides in GitHub only. You will probably need devtools for that (install.packages("devtools")).
devtools::install_github("roukoslab/bluntPred")Open the web app in your R console:
bluntPred::shiny_bluntPred()Paste a list of gRNAs targets and click on Predict.
The list can actually be a table with <tab> or <comma> separated fields.
The gRNA sequence is expected to be in the first column.
NOTE: Only the seed portion of the protospacer (this is, the last 10 nucloetides of the target sequence) are used for the prediction in this model.
If you find this tool useful and use it in your research, please cite our publication:
Longo, Sayols et al., Linking CRISPR–Cas9 double-strand break profiles to gene editing precision with BreakTag. Nat. Biotechnol. 2024. DOI: https://doi.org/10.1038/s41587-024-02238-8