I have a metagenomic dataset consisting of paired-end and singleton FASTQ files, generated after host removal and quality filtering. Specifically:
Forward reads: final_clean.1.fastq Reverse reads: _R2.fastq or _final_clean.2.fastq
Singleton reads: final_clean_single.fastq
I would like to calculate: Total number of reads Average number of reads per sample
My questions are:
1) Should I count only the forward reads (R1) to represent paired-end read counts, or do I need to include both R1 and R2?
2) How should singleton files be handled in this calculation?
3) Is there a recommended way or tool (e.g., seqkit) to do this accurately while skipping empty or corrupted files?
4) Also, some .fastq files might be 0 bytes or corrupted — how can I avoid including those in the calculation?
If you have code or any example please share with me.
Thanks.
To calculate total and average read counts, you should count both R1 and R2 files separately for paired-end reads, then add the counts from singleton files to get the overall total. Singletons are simply extra reads and should be included in the total. Tools like seqkit stats are recommended as they are fast, accurate, and work with both uncompressed and gzipped FASTQ files. Before counting, skip empty or corrupted files by checking their size with [ -s file.fastq ]. Once you have the total read count, divide it by the number of samples to get the average reads per sample.
Hello, I would encourage that you use softwares as CLC genomics for this analysis. You can manipulate your data as single and paired end reads, and when you map to reference genomes, it automatically tells how many reads there is. Matter of fact, it codes reads mapping such that regions that have only single reads mapping to them are identifiable just by looking at the mapping.
If you are going to use qiime for microbial diversity, you will get all these data directly in the denoising stats.
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