Based on the Meth_Compare project and parameters we collectively decided on in processing bisulfite sequencing data, I wanted to reprocess the salmon BS data. Before re-doing all alignments, I wanted to explore different alignment options for potential optimization.

Alignment optimization: Explore PE vs. SE mode

Alignment in SE mode typically retains more of the data than alignment in PE mode. Zymoresearch shared the parameters they use for alignment:

  • On Fri, Jul 19, 2019 at 2:19 PM Karolyn Giang wrote: For Pico Methyl-Seq libraries, we use Bismark and Bowtie1 for alignment with the non-directional alignment settings and our bioinformatics team uses this sequence for trimming purposes: GATCGGAAGAGC. For methylation calling, we use Methyldackel. If the libraries were sequenced using PE reads, we sometimes align the reads as SR to increase alignment rates. Another suggestion is to first perform the adapter trimming, and then trim the first 5 nucleotides at the beginning of read 1 and read 2. The random priming may generate artifacts that causes issues with alignment, so trimming off the first few nucleotides can help.

Alignment optimization: Explore global vs local alignment mode

local alignment mode performs soft clipping so that reads with mismatching sequence (poor quality) in the beginning or end of the read can still be retained.

  • more info
    • “explore what causes the differences, and whether or not you trust the local alignments. If, for instance, all extra alignments in local mode originate from one certain repetitive location of the genome, there is probably not much added value in using it. If you find a technical justification why you would want to use local mode in your case, and this adds additional reads everywhere equally, it might be a good idea.”



I. Descriptive stats

TrimGalore in PE or SE mode

Bowtie2 in PE vs. SE global alignment mode

  • Compared to PE alignments, SE gives:
    • ~ 5% increase in unique alignments (~700K reads)
    • ~ 3% increase in ambiguous alignments
    • ~ 7% decrease in no alignments
    • percent methylation does not change
    • ~ 7% increase in duplicated reads
    • ~ 2x increase in number of deduplicated reads

Bowtie2 in PE vs. SE local alignment mode

  • Compared to PE alignments, SE local gives:
    • ~ 20% increase in unqiue alignments (~ 3M reads)
    • ~ 7% increase in ambiguous alignments
    • ~ 28% decrease in no alignments
    • ~ 6% increase in duplicated reads
    • ~ 2.6x increase in number of deduplicated reads

Alignment stats are from PE alignment multiqc report, SE global alignment multiqc report, and SE local alignment multiqc report.

All plots below were generated by 20200424_BmrkCmpr.Rmd using the above linked multiqc_bismark_alignment.txt files from each Bismark analysis:

I also plotted this data out in a googlesheet before I could get fread to work successfully: PEvSE_trim_align_modes

II. CpG loci covered

Most loci are commonly covered no matter what mode is used. There is not a substantial gain going from PE to SE global. There is also not a big difference between SE global to SE local. It looks like there are particular genome regions gain reads in local mode (NC_027300, NC_027308, and NC_027309) rather than it being an even distribution.

All plots below are from 20200424_BmrkCmpr.Rmd and were plotted in this google sheet:Bmrk_PEvSE_CmprBeds

The plots show the average number of CpG loci exclusively and commonly identified by PE, SE global, SE local. Averages were calculated from 4 individual samples.

Restricting to 5x CpG loci covered

These plots show substantial gain in 5x CpG loci covered by SE modes (~3-4 fold more)

Is there a chromosomal bias?

No. Excluding the mitochondrial chromosome (NC_001960.1) which shows low coverage overall, chromosomes on average show:

  • 3.63 +/- 0.14 (s.d.) fold increase in number of 5x CpG loci retained with SE global mode
  • 4.73 +/- 0.20 (s.d.) fold increase in the number of 5x CpG loci retained with SE local mode


  • more examples of loci gained from SE global and SE local vs. PE. These screenshots show regions where many more reads mapped in SE mode compared to PE mode. These also happen to be repetitive genome regions.

What is the spread of coverage of loci that are retained in SE global but not identified in PE?


In the end I’m sticking with PE and following the MethCompare pipeline because:

  1. While more 5x CpG loci are gained in SE mode relatively evenly across the genome, these loci are:
    • low coverage (didn’t pass the 5x coverage threshold with PE reads)
    • moderate coverage but mapped to repetitive regions and likely artificatual
  2. I’m not convinced the local alignments are good since the local alignment score thresholding is different and likely needs to be optimized. This is discussed in this thread:
    • in local mode: –score-min G,20,8
    • in global mode: –score-min L,0,-0.6
  3. Steven has also mentioned that PE gives a read two anchor points to the genome as opposed to one which should be more accurate.

I think the MethCompare pipeline should be a good place to start and if the mapping is terrible maybe try adjusting mapping parameters or SE.