Inference of ASVs from single-end sequencing (Depreceated)
DEPRECATED: Use instead Qiime2FeatureTableExtractorSE class (old_Q2FeatureInferenceSE)
This task infers Amplicon Sequence Variants (ASVs) using the function
qiime dada2 denoise-single from Qiime2. This task starts by trimming and filtering sequences (see below) before infering ASVs with DADA2 (The Divisive Amplicon Denoising Algorithm).
About trimming sequences:
It is convenient to ensure that the quality of the reads does not fall below a PHRED score at 25 (corresponding to 1 incorrect base over a length of 320). To avoid problems in the determination of chimeras it is convenient to eliminate the first nucleotides as they may correspond to the primers that have been used in the 16S amplification.
truncated_reads_size refers to the position at which read sequences should be truncated due to decrease in quality. This truncates the 3' end of sequences (i.e. the right side).
5_prime_hard_trimming_reads_size refers to the position at which read sequences should be trimmed due to low quality. This trims the 5' end of the input sequences (i.e. the left side).
5_prime_hard_trimming_reads_size are provided, filtered reads will have length
With the following sequence of 10 nucleotides ATCATCATCG, using
truncated_reads_size at 8 and
5_prime_hard_trimming_reads_size at 2 will result in a sequence of 6 nucleotide CATCAT.
Dada2 turns single-end sequences into denoised, chimera-free, inferred sample sequences. The core denoising algorithm is built on a model of the errors in sequenced amplicon reads. For more information about Dada2, we suggest to read Benjamin J. Callahan et al., 2016 (https://www.nature.com/articles/nmeth.3869)
Number of threads
Read size to conserve after quality PHRED check in the previous step