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  • Yes. Thank you.

    Also, after trimming my fastq files using default parameters on Trim galore, I continue to see truseq adapters (under the overrepresented sequences tab). eg. ATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGGCCATCTCGTATGCCGTCTTCTGCTTGAAAAAAAA

    Does Trim_galore not recognize all adapters? Or should I explicitly provide these adapters to Trim_galore?

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    • Trim Galore tries to identify read-through adapter contamination, which is the kind of contaminant that will prevent your sequences from aligning at all, or might even cause mis-alignments. For the standard Illumina adapter, this sequence always starts with AGATCGGAAGAGC...

      It does not attempt to find or remove any other kind of contaminant or over-represented sequence, because they are normally not as harmful. E.g. an over-represented sequence such as ATCGGAAGAGCACACGTCTGAACTCCAGTCACATGAGGCCATCTCGTATGCCGTCTTCTGCTTGAAAAAAAA might be present in the library, but it will simply not align to a genome, thereby filtering itself out at the alignment stage.
      Last edited by fkrueger; 01-18-2019, 02:24 AM. Reason: typo

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      • Strange peak at beginning of M-bias plots

        Hi,

        I have generated RRBS read data, which I have filtered and trimmed using Trim Galore and then aligned to a reference genome using Bismark. Three independent sequencing libraries were created with samples randomly mixed among the three sequencing libraries.

        All the individuals from one of the libraries have a characteristic peak in their m-bias plots (see attached Bad_M-bias image), where there is a spike in methylation in the first 5 nucleotide positions, before settling at a level of around 60 % CpG methylation. The M-bias plots from samples from the other libraries show relatively stable CpG methylation levels at 60% with no spike at the beginning (see attached Good_M-bias image).

        This unusual M-bias profile is also accompanied by a drop in q-value in the same nucleotide position for all the samples from this one specific library.

        I had previously ignored this issue as the drop in quality was not severe but this has lead to some issues with downstream analyses (for example SNP-calling from the RRBS reads) that has lead me to revisit my read QC and all these issues concern samples from this particular library.

        To this end, does anyone know what could be causing these issues with samples from this specific library and how to go about solving this problem? Would simply trimming the 5' end of the reads in Trim Galore! for all samples regardless of library remedy this problem or would the issues be deeper than this?

        Thanks in advance!
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