SCRAMble-MEIs Caller

Repository Layout

1. src/cluster_identifier.c - the application responsible for identifying soft clipped clusters. For how to build see the build section.
2. src/SCRAMble-MEIs.R - the analysis of soft-clipped clusters and MEIs events.

Build

Install dependencies (Ubuntu 20.04):

apt-get update
apt-get install -y  \
    autoconf \
    autogen \
    build-essential \
    curl \
    libbz2-dev \
    libcurl4-openssl-dev \
    libhts-dev \
    liblzma-dev \
    libncurses5-dev \
    libnss-sss \
    libssl-dev \
    libxml2-dev \
    ncbi-blast+ \
    r-base \
    r-bioc-biostrings \
    r-bioc-rsamtools \
    r-cran-biocmanager \
    r-cran-devtools \
    r-cran-stringr \
    r-cran-optparse \
    zlib1g-dev

Install R packages dependencies:

Rscript -e "library(devtools); install_github('mhahsler/rBLAST')"

To build the cluster_identifier (estimated install time <5 minutes):

$ cd src
$ make

That should be it. It will create an executable named src/cluster_identifier.

Running

SCRAMble runs as a two-step process. First cluster_identifier is used to generate soft-clipped read cluster consensus sequences. Second, SCRAMble-MEIs.R analyzes the cluster file for likely MEIs. Running SCRAMble on the test bam in the validation directory should take <1 minute for each step.

To run SCRAMble cluster_identifier:

$ /path/to/scramble/src/cluster_identifier -@ thread \
    /path/to/test.bam > /path/to/test.clusters.txt

$ /path/to/scramble/src/cluster_identifier -@ thread \
    -R /path/to/genome/human_genome.fasta /path/to/test.cram > /path/to/test.clusters.txt

To run SCRAMble-MEIs(with default settings):

$ Rscript /path/to/scramble/src/SCRAMble-MEIs.R \
    --mei-refs /path/to/scramble/genome/MEI_consensus_seqs.fa \
    --ref /path/to/genome/human_genome.fasta
    --cluster-file /path/to/test.clusters.txt \
    --out-file /path/to/test.vcf.gz 	\

Output

The output of cluster_identifier is a tab delimited text file with clipped cluster consensus sequences. The columns are as follows:

1.	Coordinate
2.	Side of read where soft-clipped occurred
3.	Number of reads in cluster
4.	Clipped read consensus
5.	Anchored read consensus

Calling SCRAMble.R with --eval-meis produces a tab delimted file. If a reference .fa file is provided, then a VCF is produced as well. The <out-name>_MEIs.txt output is a tab delimited text file with MEI calls. If no MEIs are present an output file will still be produced with only the header. The columns are as follows:

1.	Insertion	Coordinate where MEI insertion occurs (zero-based)
2.	MEI_Family	The consensus sequence to which the clipped sequence aligned best
3.	Insertion_Direction	Whether MEI is on fwd or rev strand relative to bam reference
4.	Clipped_Reads_In_Cluster	Number of supporting reads in cluster
5.	Alignment_Score	Pairwise alignment score of clipped read consensus to MEI reference sequence
6.	Alignment_Percent_Length	Percent of clipped read consensus sequence involved in alignment to MEI reference sequence
7.	Alignment_Percent_Identity	Percent identify of alignment of clipped read consensus sequence with MEI reference sequence
8.	Clipped_Sequence	Clipped cluster consensus sequences
9.	Clipped_Side	Left or right, side of read where soft-clipping ocurred
10.	Start_In_MEI	Left-most position of alignment to MEI reference sequence
11.	Stop_In_MEI	Right-most position of alignment to MEI reference sequence
12.	polyA_Position	Position of polyA clipped read cluster if found
13.	polyA_Seq	Clipped cluster consensus sequences of polyA clipped read cluster if found
14.	polyA_SupportingReads	Number of supporting reads in polyA clipped read cluster if found
15.	TSD	Target site duplication sequence if polyA clipped read cluster found
16.	TSD_length	Length of target site duplication if polyA clipped read cluster found

Disclaimers

In theory, SCRAMble should work well on any MEI reference fasta sequences, however, it has only been tested on the sequences provided in /path/to/scramble/genome/MEI_consensus_seqs.fa.