spritefridge 1.4.0

A python toolbox for processing SPRITE-seq within the cooler universe

spritefridge

A python toolbox for processing SPRITEseq data

Installation

To be able to run everything correctly we need a few prerequisits installed especially bedtools. Furthermore, at the time of writing this some dependencies refused to compile when installing with pip (krbalancing). Installing these is easiest done using conda. For convenience we provide an environment file (env.yml) with this package Installation thus works like

conda env create -f env.yml
conda activate sprite
pip install spritefridge

Usage

spritefridge comprises five tools to process and annotate SPRITE-seq data and results. Below are some example commands. For more details please refer to the generated help messages spritefridge <subcommand> -h

extractbc

extractbc aims to extract barcodes from reads according to a list of used barcodes and barcode layouts (i.e. how the barcodes are aranged in read sequence) A typical command looks like this

spritefridge extractbc \
    -r1 r1.fq.gz \
    -r2 r2.fq.gz \
    -bc barcodes.tsv \
    -l1 DPM \
    -l2 'Y|SPACER|ODD|SPACER|EVEN|SPACER|ODD' \
    -m 'DPM:0,Y:0,EVEN:2,ODD:2' \
    -o out.bcextract.fq.gz \
    -p 4

This command will read in the barcodes and the try to find barcodes in the respective read sequence in the order given by the layouts starting from 5' end. -m gives the allowed mismatches for the barcode identification. In addition to out.bcextract.fq.gz which contains reads with the extracted barcodes appended to their names, the tool also outputs statistics for how many reads were found with 1, 2, 3, ... barcodes. -p specifies the number of processes to use for extraction. -l1 and -l2 can also be left empty if barcodes are only to be extracted from one read.

pairs

pairs identifies barcode clusters from aligned reads and writes them into pairs files for each cluster size

spritefridge pairs \
    -b in.bam \
    -o pairs/out \
    -cl 2 \
    -ch 1000 \
    --separator '['

This command will read in alignments from in.bam (needs to be filtered for multimappers and quality) groups the reads by barcodes and then writes all possible pairs for each cluster of sizes between 2 and 1000 reads to a file named pairs/out_<clustersize>.pairs. This tool also outputs a dedicated bedfile containing all reads from each cluster to be used to annotated the Cooler bins later on (see annotate). Additionally, one can specify the a list of barcode name prefixes to ignore when generating the clusters via --ignoreprefix e.g. when having RPM and DPM sequences present which should really be in the same cluster (--ignoreprefix "RPM,DPM")

combine

combine merges cool files generated from cluster pairs files according to the SPRITE-seq recommendation by multiplying the counts of each Cooler by 2/n, where n is the cluster size, before merging. The cluster size is inferred from the file name which needs to be of the pattern <name>_<clustersize>.cool

spritefridge combine \
    -i coolers/* \
    -o merged.cool \
    --floatcounts

--floatcounts ensures that merged counts are stored as float and not be casted to int

annotate

annotate takes in a bedfile (see pairs) and annotated each bin with the overlapping reads of each cluster.

spritefridge annotate \
    -i merged.mcool \
    -b clusters.bed

merged.mcool is a zoomified version of the merged.cool file

balance

balance is used to balance the contact matrices of the resulting mcool file using iterative correction and Knight-Ruiz matrix balancing genomewide and per chromosome

spritefridge balance \
    -m testdata/sprite.new.mcool \
    -p 2 \
    --overwrite

-p specifies the number of processes to use for iterative correction and --overwrite will overwrite any existing weights with the same name in the Cooler