Question: ATAC-seq fragment length distribution
0
gravatar for Grinch
6 weeks ago by
Grinch10
Germany
Grinch10 wrote:

Hi,

I'm analyzing human cancer cell line ATAC-seq data (PE 75bp Illumina) and have encountered a weird insert size distribution. For ATAC-seq PE data, insert size distribution is a good quality control of how well the experiment worked (This is how it would ideally look https://dbrg77.wordpress.com/2017/02/10/atac-seq-insert-size-plotting/). I have used several mapping strategies and always come to a similar distribution plot.

Sequencing is good quality with 50-60 million reads per sample with min. 3 replicates and mapping >80% for all samples. > 95% of forward and reverse reads are paired.

Analysis

java -jar $tool_path/trimmomatic-0.36.jar PE -phred33 -threads 20 $forward $reverse $forward_1_paired.fastq $forward_unpaired.fastq $reverse_paired.fastq $reverse_unpaired.fastq ILLUMINACLIP:$tool_path/adapters/TruSeq3-PE.fa:2:30:10 LEADING:10 TRAILING:10 MINLEN:50

bowtie2 --threads 20 --very-sensitive -x $bowtie2index -1 $forward_paired -2 $reverse_paired -S $filename_aligned.sam

Plotting

samtools view ATAC_f2q30_sorted.bam | awk '$9>0' | cut -f 9 | sort | uniq -c | sort -b -k2,2n | sed -e 's/^[ \t]*//' > fragment_length_count.txt

Alternative way of plotting

java -Xmx20g -jar picard.jar CollectInsertSizeMetrics I=$file O=insert_size_metrics.txt H=insert_size_histogram.pdf

Insert size distribution *In the plot it should be written insert length, not fragment length.

Does anyone have an idea what went wrong here? Could this data be still useful to analysis? I was thinking to extract PE reads with insert size between 0-300nt and proceed only with these reads for downstream analysis?

atac-seq • 210 views
ADD COMMENTlink modified 6 weeks ago • written 6 weeks ago by Grinch10

Please share your code for adapter trimming, mapping and generating this figure. Then we'll see. Still, this looks suspicious and I've never seen it in our ATAC-seq data, be it mouse or human.

ADD REPLYlink modified 6 weeks ago • written 6 weeks ago by ATpoint7.5k

I've added the information in the description.

ADD REPLYlink written 6 weeks ago by Grinch10

The only odd thing I see is that you trimmed for the TruSeq instead of the Nextera adapter. Sequence to trim on both ends is CTGTCTCTTATACACATCT. This makes a difference when calculating insert sizes, but typically the nucleosomal pattern should still be visible. How did the gel or Bioanalyzer picture look prior to sequencing (hope you did that quality control)?

ADD REPLYlink written 6 weeks ago by ATpoint7.5k

Hi , There is few question to ask ; -is your sequencing is good ? ( high coverage of you insertion ? ) -is your model organism got a "complete" genome ref ? ( correct assembly? high concentration of TE/ high genome dynamic ? )

ADD REPLYlink written 6 weeks ago by Titus720

I've added the information in the description.

ADD REPLYlink written 6 weeks ago by Grinch10

So you are with data with hi heterogeneity/ high rearrangement because of cancer cells , did you check with for example IGV the coverage of one insertion ?

ADD REPLYlink written 6 weeks ago by Titus720
1

I do not think that the cell-of-origin is the reason here. We've done ATAC-seq in different cancer cell lines, and the pattern was always clear and distinct. ATAC-seq still looks at the genome/regulome, and single rearrangements IMHO do not influence the overall picture.

ADD REPLYlink written 6 weeks ago by ATpoint7.5k

Ok i taught it could explain the differences ...

ADD REPLYlink written 6 weeks ago by Titus720

Was the plot from your custom code or the data from Picard? What does the output of bamPEFragmentSize from deepTools look like? What does the output of samtools idxstats look like? Are you sure this is actually your sample and not someone else's (it happens, though rarely)?

ADD REPLYlink written 6 weeks ago by Devon Ryan84k
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