# Tag Info

9

If you check the read QC statistics of an Illumina run in e.g. fastQC, you will see that at the end of the read the quality decreases. This is because of exhaustion of chemicals at the end of the run. This is a general trend seen in all runs, therefore you can remove these low quality bases from the end of your run. If you have incidentally a bad quality ...

6

I am commenting on this part: The algorithm only removes low quality bases from the end until it reaches a good quality base. If there is a bad quality base beyond that, it is not trimmed. According to its user guide, cutadap is designed this way: it trims off bases from the 3'-end until it sees a base with quality higher than a threshold. This is not a ...

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As @AaronBerlin mentioned, you didn't remove reads that were completely trimmed. Next time use the --minimum-length option and set it to something reasonable, like 20. Alternatively, use "Trim Galore!", which is a wrapper around cutadapt that has more reasonable defaults.

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To answer your direct question, there are a few reasons why there might be high levels of sequence duplication. From the FastQC help: The underlying assumption of this module is of a diverse unenriched library. Any deviation from this assumption will naturally generate duplicates and can lead to warnings or errors from this module. As @DevonRyan ...

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FastQC assumes that all samples are for whole genome sequencing and will flag them as failed if they differ too much from that assumption. This will, for example, cause essentially all RNA-seq, ChIP-seq, and ATAC-seq samples to fail in one module or another. This is not any cause for concern and is completely expected. Primarily concern yourself with whether ...

5

If you want to stick to grep, use a scripting language such as Perl to generate the regex programmatically. For example: perl -le 'print join "", map "[${_}N]", split //,$ARGV[0];' ATCGCTATCG Prints: [AN][TN][CN][GN][CN][TN][AN][TN][CN][GN] You can use it in grep like so: grep '[AN][TN][CN][GN][CN][TN][AN][TN][CN][GN]' <<< ...

4

This indeed looks like some sort of binary file. I have seen bunch of those when people renamed .fastq.gz to .fastq without actually uncompressing them. However, as you say, this is not your case, as file pair1.fastq would identify the file as gzip compressed data. Looking at possible return values of file comments here, data basically mean the command could ...

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First the Nextera adapters and the custom barcode adapters overlap each other Nextera 1 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG Barcode 1 GATACGGCGACCACCGAGATCTACACTAGATCGCTCGTCGGCAGCGTCAGATGTGTAT Nextera 2 GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG Barcode 2 ...

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It's not an unreasonable approach. rMATs is rather picky about its input, but it seems you've noticed that. rMATs can't handle trimmed reads. It also can't handle soft-clipped reads. You might as well not trim. You'll get a lower alignment rate, but your only other choice would be to either exclude the trimmed reads or trim everything down to the same length....

4

You mention that FastQC "fails to find the actual adapter sequences" - I guess you mean in the Adapter Sequence Contamination plot. However, the kmer and Sequence Content Plots are often useful even when the former fails. I've used these in the past - you can sometimes just read off the adapter sequence from the start of the Sequence Content Plot (or at ...

4

You can have a look at cutadapt. It is capable of quality trimming for both ends as you can read here.

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I never spent too much time on choosing my trimming software, therefore I might have missed some jewels. I use trimmomatic when I need versatility and I am entirely sure that it trims reads on both ends. A more convenient option is skewer, they do perform quality filtering, but I could not find an explicit confirmation that it works on 5' too.

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The command where you trim with adapters and by quality is perfectly fine. That FastQC isn't perfectly happy is expected. It's a tool made for whole genome sequencing QC, you SHOULD see a number of "fail"s with any RNA-seq protocol. Steps that should always fail in RNA-seq: per-base sequence content (there's "random priming" performed that isn't completely ...

2

I'm not aware of any existing methods to do this, but here are a couple of ideas about how it might be done: Canu has a method of adapter trimming which involves looking for the absence of overlap for reads. If there are no other reads which share sequence across a particular region, then the read is broken up at the point of low coverage, and small pieces ...

2

Alternatively, I really like using bbduk which is part of the BBMap suite. I've processed every nascent sequencing dataset that has been published, and found a lot of quirky errors with older datasets using TrimGalore. bbduk is a little more fine-tuneable relative to cutadapt/trimmomatic/trimGalore (built on top of cutadapt)/fastp and the run time and ...

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You're best off just using fastp or Trim Galore!, both of which will determine the adapter sequence for you. Trim Galore! uses a built-in list of known sequences for this, whereas fastp uses read overlap.

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Out of all of the major trimming tools available and widely used (trimmomatic, cutadapt/trimGalore (trimGalore is built on top of cutadapt), fastp), I actually instead prefer bbduk which is part of the bbmap suite: https://github.com/BioInfoTools/BBMap https://github.com/BioInfoTools/BBMap/blob/master/sh/bbduk.sh It's much more "fine tuneable" than the ...

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Depending on the cluster management tool, you might have received e-mails when the "job" begins and ends. If so, you can check the "Exit status" of the job. For example, in the case of our HPC the relevant lines from the "job completion e-mail" would be: Execution terminated Exit_status=0 BTW, at first I was confused with the phrase "Execution terminated", ...

1

For Illumina (and 454) reads, the quality decreases with read length. It's not linear and is run/library-dependent. It has less to do with exhaustion of reagents and more to do with the strands on a spot being out of phase due to incomplete/missed base incorporation during sequencing. It is common practice to trim off the low quality 3' ends as the ...

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The minion utility from the kraken/reaper toolkit may be helpful for this: http://wwwdev.ebi.ac.uk/enright-dev/kraken/reaper/src/reaper-latest/doc/minion.html

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If you happen to know a sequence that should be highly abundant in the library, you can grep its beginning or end (with pattern match highlighting) and see if the same sequence systematically comes just before or just after respectively. This kind of visual inspection can help you finding the adaptor. For instance, in a previous lab, we were working on D. ...

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