Losing high percentage of reads with good quality scores

For my data, I don't use too many data treatings, because the returned data of Sequencing company has been taken care of before import.
So my import method as follows:
qiime tools import \

--input-path manifest.csv \

--type SampleData[SequencesWithQuality] **

--input-format SingleEndFastqManifestPhred33 **

--output-path rdl_data.qza**


Try importing again, first with the --input-format set to SingleEndFastqManifestPhred33V2 and if that does not work try SingleEndFastqManifestPhred64V2.

I try the two ways, but they don't work, and SingleEndFastqManifestPhred33 is the ueseful importing way.


Gahh, I forgot that the V2 signifies how the manifest file is built, try keeping everything else the same but changing the 33 to 64. It still may not work but lets try all the options here.

Yes, it do not work and provide a error information in the picture.
I think the ultimate reason that maybe my data really not good.

Thanks for your help. For my data, I can observe multiple sequences with at least 90% similarity, and part of thses sequences that quality score only have single nucleotide differences. But I not sure this is main reason.

An off-topic reply has been split into a new topic: Losing high percentage of reads in dada2 denoise-paired

Please keep replies on-topic in the future.


I was able to take a closer look at your data and it looks like it may not have been collected on an Illumina machine, based on the values of the PHRED scores present. The scores you have in your data contain a wider range of values than would be present in a single Illumina variant on its own, as well as having longer reads than would be expected with the standard Illumina reads. Do you know what technology was used to sequence your data? If not could you ask your sequencing center?

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Hi, thanks for your reply, I ask the sequencing center about the sequencing methods. The company uses Pacbio SMRT to sequence my data.

@ZHY, in that case, I think you will use your original imports then and change the denoising method to denoise-ccs (docs), which was added in the last release.


Hi, I try to use the denoise-ccs, but my data have been dealt with, it does not contain front and adapter, so I wonder if the qiime2's development team could consider adjust the parameters' setting. And I also try to ask for the original data, but it has been a little long, the original data may be deleted.
Thanks for your help.

@ZHY I will have to look into that, the denoise-ccs method is a recent addition and I think generally the adapters and primers are still included, but do not see why they would have to be. These are not required parameters for other tools in q2-dada2, so I do not think they need to be required here, but need to check to make sure I am not missing something.

Thanks for your reply and help, I think I will have a wonderful travel with Qiime2. And most of all, thank you for your efforts. I will keep an eye on it for updates.

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