I went through a couple of posts before posting this question here. I have ATAC-seq paired-end samples and did the following steps but always end up with blue peaks in the insert size metrics. I found a similar post in biostars but did not find a solution to that, and there was also a post in galaxy (https://galaxyproject.github.io/training-material/topics/epigenetics/tutorials/atac-seq/tutorial.html) with the blue peaks that explains that those blue peaks are reverse forward-oriented . I am not sure if am making an error in the pipeline or is at the sequencing level? Could someone take a look at the script to see where the mistake might be and if anything should be different here? This is my first time with ATAC-Seq data. GrCH38 was used as the ref genome. I see a lot of forums with the same question but no solution. Could an ATAC expert help so that all of us learners could benefit. Thanks so much. Below are the steps I followed:
# remove the nextera adapter sequences
cutadapt -q 10 -a CTGTCTCTTATA -A CTGTCTCTTATA --minimum-length 25 -o sample_P1.fastq -p sample_P2.fastq sample_R1_001.fastq sample_R2_001.fastq
#mapping using Bowtie2
bowtie2 -p 4 -q --very-sensitive -x genome -1 sample_P1.fastq -2 sample_P2.fastq -S sample_aln_unsorted.sam
#remove MT chromosomes
grep -v 'chrM' sample_aln_unsorted.sam | samtools view -b -h -F 4 -f 0x2 - >sample_aln_unsorted.bam
#sam to bam
java -jar picard.jar SortSam I=sample_aln_unsorted.bam O=sample_aln_sorted.bam SORT_ORDER=coordinate
##mark duplicates
java -jar picard.jar MarkDuplicates INPUT=sample_aln_sorted.bam OUTPUT=sample_aln_sorted.rem.bam M=sample.txt REMOVE_DUPLICATES=true
##index
samtools index sample_aln_sorted.rem.bam
##insertsizemetrics
java -jar picard.jar CollectInsertSizeMetrics I=sample_aln_sorted.rem.bam O=sample_metrics.txt H=insert_size_sample_histogram.pdf

0 answers
No answers yet.
Log in to answer this question.
Have you seen this previous extensive discussion? Insert Size Metrics of ATAC-Seq data
Yes, I did but most of the comments were on how not to trim the reads, and that there might be issues with the low-level processing fo the data. So just posted all that I did to see if there is a problem with the processing or should I trace it back to the sequencing facility.
I am not sure there is any problem with the data based on that plot alone. If your fragments are shorter than your reads, Bowtie would consider those discordant. If you change the Bowtie settings or trim the reads, that would change how the reads are classified.
What would be the best bowtie setting for such a situation? I did go through the previous post and tried out a couple of settings.