ICRMax Documentation Release 1.0 BioInfo MOCHSL Jul 23, 2018 Contents 1 What is ICRmax? 1 2 How ICRmax works 3 3 Benefits 5 4 Requirements 7 5 Overview 9 6 Preparing the WGS reads 11 7 Step-by-step command line 13 8 Initial ICRmax output 15 9 Removing recurrent events 17 10 Detecting new recurrent events in your samples 19 11 Visualizing output in Circos plots 21 12 Parameters 25 13 Simulate Rearranged Genomes 27 14 Updates 29 15 Data Access 31 16 Acknowledgements 33 17 Contact 35 18 References 37 i ii CHAPTER 1 What is ICRmax? ICRmax is a computational pipeline designed for the cost-effective identification of a minimal set of tumor-specific interchromosomal rearrangements (ICRs) for clinical application. 1 ICRMax Documentation, Release 1.0 2 Chapter 1. What is ICRmax? CHAPTER 2 How ICRmax works ICRmax was developed with a set of strict filters to eliminate false positive ICR events. The pipeline is able to remove most cases of non-somatic events without the need for sequencing the matched normal genome for each sample. 3 ICRMax Documentation, Release 1.0 4 Chapter 2. How ICRmax works CHAPTER 3 Benefits The cost reduction resulting from this approach creates an opportunity to implement this analysis in the clinical setting, mainly for detection of personalized biomarkers that can be used in the management of solid tumors. 5 ICRMax Documentation, Release 1.0 6 Chapter 3. Benefits CHAPTER 4 Requirements All the steps necessary for the ICRmax pipeline can be performed using open-source software/pipelines and publicly available data. The pipeline was executed in our servers running Ubuntu 14.04.1 LTS. Software and pipelines: • A computer running Linux (suggested distributions: Ubuntu, Fedora or CentOS. ICRMax should work on all Linux distributions). • BLAT (how to install: http://users.soe.ucsc.edu/~kent/src/) • Bedtools (how to install: http://bedtools.readthedocs.org/en/latest/) Data: • Alternative genome assemblies (see Preparing the WGS reads). • Recurrent artifacts (download here recurrent_artifacts.bed or recurrent_1000G.bed). • Repetitive regions to filter (download here centr_and_tel.bed and all_to_mask.bed) • Whole genome sequence alignment data in BAM or BED format (see below for details). To transform paired bam files into bedpe use: $ bedtools bamtobed -bedpe <input.bam> Example bedpe file: 7 ICRMax Documentation, Release 1.0 8 Chapter 4. Requirements CHAPTER 5 Overview To run ICRmax simply follow the Step-by-step command line summarized in the figure below: 9 ICRMax Documentation, Release 1.0 10 Chapter 5. Overview CHAPTER 6 Preparing the WGS reads Mate-pair (usually from SOLiD platform) or paired-end (usually from Illumina platform) reads resulting from whole genome sequencing must be aligned to the reference genome. Users are free to choose the best mapping algorithm for their platform. Suggestions are NovoAlignCS or BioScope for SOLiD mate-pair reads and BWA or Bowtie2 for Illumina paired-end reads. Alignment to alternative reference assemblies is also advised since differences in assembly can give rise to mate-pair reads mapped in different chromosomes according to one assembly but not another. The alternative assemblies that can be used for mapping are: HuRef (J. Craig Venter Institute) [Levy et. al 2007] GRCh37_alt (Partial reference genome with alternative representations – Genome Reference Consortium) CRA (Human chr7 complete sequence – The Center for Applied Genomics) [Scherer et al. 2003] Note: For the alternative assemblies, use as input only the reads belonging to mate-pairs that mapped in different chromosomes in the initial reference genome alignment. There is no need to realign the reads that have reliable mappings to the same chromosome. 11 ICRMax Documentation, Release 1.0 12 Chapter 6. Preparing the WGS reads CHAPTER 7 Step-by-step command line At this step you should have a paired BED file (bedpe) containing the aligned mate-pair or paired-end reads mapped in different chromosomes with mapping quality greater than or equal to 20, after the reference genome mapping and mapping to alternative reference assemblies. The duplicate reads should also have been removed. For that, samtools rmdup is a good option (see http://www.htslib.org/man/samtools) 1. Remove reads mapped in the mitochondrial chromosome and order the bed file: $ grep -v 'chrM' input.bed | sortBed > step1_woM.bed 2. Remove reads mapped in centromere and telomere regions. To do that on both reads in the mate pair you must invert the file and repeat the command. Download the file with centromere end telomere positions centr_and_tel.bed: $ bedtools subtract -A -a step1_woM.bed -b centr_and_tel.bed > step2.wo_ ,!centr_tel.bed $ awk '{print $4,$5,$6,$1,$2,$3,$7,$8,$10,$9}' step2.wo_centr_tel.bed | sed ,!"s/\s/\t/g" | sortBed > step2.wo_centr_tel.inv.bed $ bedtools subtract -A -a step2.wo_centr_tel.inv.bed -b centr_and_tel.bed > ,!step2.wo_centr_tel.final.bed 3. Remove reads mapped in masked regions. Download the file with regions to mask all_to_mask.bed: $ bedtools subtract -A -f 1.0 -a step2.wo_centr_tel.final.bed -b all_to_mask. ,!bed > step3.masked.bed $ awk '{print $4,$5,$6,$1,$2,$3,$7,$8,$10,$9}' step3.masked.bed | sed "s/\s/ ,!\t/g" | sortBed > step3.masked.inv.bed $ bedtools subtract -A -f 1.0 -a step3.masked.inv.bed -b all_to_mask.bed | ,!sortBed > step3.masked.final.bed 4. Cluster the reads from different mate pairs mapped in the same chromosome. Use mean insert size +2s.d. as cluster distance (-d size, e.g. 1000). At this point observe that a cluster number will be generated and the file will have an extra column ($11): 13 ICRMax Documentation, Release 1.0 $ bedtools cluster -i step3.masked.final.bed -d 1000 > step4.cluster.bed $ awk '{print $4,$5,$6,$1,$2,$3,$7,$8,$10,$9,$11}' step4.cluster.bed | sed ,!"s/\s/\t/g" | sortBed > step4.cluster.inv.bed $ bedtools cluster -i step4.cluster.inv.bed -d 1000 > step4.cluster.final.bed 5. Join cluster numbers generated for both sides and select only clusters with 3 or more reads: $ sed -i "s/\t/_/11" step4.cluster.final.bed $ awk '{print $11}' step4.cluster.final.bed | nsort | uniq -c | awk '{if ($1> ,!=3) print $2}' > clusters_over_3_reads $ fgrep -w -f clusters_over_3_reads step4.cluster.final.bed > step5_cutoff3. ,!bed 6. For SOLiD platform we suggest realigning the reads with BLAT using as input the sequences resulting from the initial alignment, BLAT parameters are the same used as default in the webtool: $ blat -stepSize=5 -repMatch=2253 -minScore=24 -minIdentity=80 -noTrimA - ,!fine -out=pslx genome.2bit input.fa Parse BLAT results and remove reads mapped in the same chromosome after alignment. 7. Invert the read order in the BED file once again and recluster the reads once more, this step should remove any clusters containing large gaps from reads that were removed by the BLAT filter. After that select only clusters still represented by at least 3 reads, at this point there are three numbers in the cluster id: $ awk '{print $4,$5,$6,$1,$2,$3,$7,$8,$10,$9,$11}' step6_BLAT_filter.bed | ,!sed "s/\s/\t/g" | sortBed | bedtools cluster -d 1000 > step7_recluster.bed $sed -i "s/\t/_/11" step7_recluster.bed $awk '{print $11}' step7_recluster.bed | nsort | uniq -c | awk '{if ($1>=3) ,!print $2}' > clusters_over_3_reads $fgrep -w -f clusters_over_3_reads3 step7_recluster.bed > step7_recluster_ ,!cutoff3.bed 14 Chapter 7. Step-by-step command line CHAPTER 8 Initial ICRmax output Example initial output: Cluster ids should have 3 numbers (eg: 14774_4005_1). Each cluster id groups reads mapping around one ICR breakpoint. 15 ICRMax Documentation, Release 1.0 16 Chapter 8. Initial ICRmax output CHAPTER 9 Removing recurrent events The final clusters of mate-pair reads can be merged at this stage so that each event has two coordinates, one in each chromosome. The command below will separate the read coordinates (one line for each chromosome) and with the simple perl script the overlapping reads will be joined into a single coordinate for each chromosome. Download here merge_bed_reads.pl $ awk '{print $1"\t"$2"\t"$3"\t"$11"\n"$4"\t"$5"\t"$6"\t"$11}' step7_recluster_ ,!cutoff3.bed > reads_in_final_clusters.bed $ perl merge_bed_reads.pl reads_in_final_clusters.bed > merged.bed The merged.bed file can then be used to check for recurrent artifacts and remove them. With bedtools intersect you can compare both your file and the recurrent artifact list, and with the subsequent awk commands you select only the events with both chromosome positions equal to a single other event in the artifact list. The output contains only the ids for the events you should remove from your final list. As a list of recurrent rearrangements you can use either one of the downloaded files (or both): recurrent_artifacts.bed (list of recurrent artifacts found in our tumor samples) recurrent_1000G.bed (list of recurrent artifacts found in 3 or more 1000G individuals) $ bedtools intersect -wo -a merged.bed -b recurrent_artifacts.bed | awk ‘{print $1,$4, ,!$8}’ | sort | uniq | awk ‘{print $2,$3}’ | sort | uniq -d | awk ‘{print $1}’ | sort ,!| uniq > recurrent_merged.bed $ fgrep -w -v -f recurrent_merged.bed merged.bed > merged_final.bed 17 ICRMax Documentation, Release 1.0 18 Chapter 9. Removing recurrent events CHAPTER 10 Detecting new recurrent events in your samples Comparison between rearrangements from different samples can be easily done with the bedtools merge command as used above, make sure to allow for a distance similar to the clustering distance used (-d 1000) outside of the read span and alter the cluster names to include sample identification (ex: 14774_4005_1_RT2). This way, after the bedtools merge command using the parameters –nms you should have a single cluster and the different cluster names separated by a semicolon. $ sortBed all_sample_rearrangements.bed | bedtools merge -d 1000 -nms > merged_ ,!samples.bed To process this file a simple perl script is used (download here find_recurrent.pl) $ perl find_recurrent.pl merged_samples.bed > tmp_file $ awk -F “\t” ‘{print $3}’ tmp_file | sort | uniq -c | awk ‘{if ($1>=2) print $2}’ > ,!recurrent_in_two_or_more_samples $ fgrep -w -v -f recurrent_in_two_or_more_samples tmp_file | awk ‘{print $1}’ > final_ ,!non_recurrent_list 19 ICRMax Documentation, Release 1.0 20 Chapter 10.