The bioBakery help forum

MINLEN question

  1. What does it mean with “MINLEN is set to 50 percent of total input read length”? Is the MINLEN different from each sample?

  2. Different version is with different default of MINLEN, right? Our version is kneaddata/0.7.2. I think the default MINLEN is 50, right?

Thanks for reaching out to us.

  1. When MINLEN is set to 50 percent of total input read length, it is filtering out for bad reads, so if 50% of the read doesn’t make quality control(QC) scores of better than X it will just be removed.

  2. With the version kneaddata/0.7.2, the default is 50. Default has been set to 50 from the version 0.7.0 release.

Let me know if you have further questions and feel free to update this thread.

Hi Sagun,

Thank you for your answer. As for the first question, do you mean the “quality control score” as the quality per base, and if the quality of 50% bases in one read less than X, this read will be removed?
When I run with the default, I found that the MINLEN number is variable across samples in the log file? When I set the MINLEN parameter, all the samples will have the same value for MINLEN in log file. Why?

Thank you for your anwer.
Wang

Yes, that is accurate for the first part. MINLEN is computed each time, based on the read length of the sample. You can set the MINLEN length by using “–trimmomatic-options” flag.

I will need some more details to replicate your issue. Can you tell me how you are setting the value MINLEN ?

Thanks,
Sagun

Hi Sagun,
Thank you for your answer. The code I used for paired-end reads as below:
"kneaddata --input R1.fastq --input R2.fastq --output kneaddata_output --bowtie2 database_folder --trimmomatic-options “SLIDINGWINDOW:4:20 MINLEN:60”. For example, if the MINLEN is set to 60, all the samples in the log file will show MINLEN 60. May I ask why?
When I run with the default, I found that the MINLEN number in the log file is different across samples. I think this is correct as you said “MINLEN is computed each time” for each sample, right?
Thanks.
Wang