信息来源>https://www.drive5.com/usearch/manual/global_trimming_and_abundance.html

Defining abundance when sequence length varies

Calculating unique sequence abundance is problematic when reads of the same template sequence vary in length, e.g. because reads are truncated when the quality score drops below a threshold.

Consider two reads A and B where B is shorter but otherwise identical to A. Here, abundance could be defined in three different ways.

(1) There are two unique sequences A and B, each with abundance one.

(2) There is one unique sequence A with abundance two.

(3) There is one unique sequence B with abundance two.

All of these definitions have problems.

With (1), a given template sequence with high abundance in the amplicons will typically have many different unique sequences with low abundances because its reads are truncated to many different lengths.

With (2) the unmatched tail of A is considered to have the same abundance as the prefix of A that is identical to B. The tail has no support from other reads (it is effectively a singleton), but that information is lost and in practice long reads with noisy tails are assigned high abundances.

With (3), the shortest sequence in a set is supported by longer sequences. This is the least bad definition: if the abundance is high, the sequence is likely to be correct. However, phylogenetically and phenotypically informative bases may be lost, and the ambiguities inherent in comparing sequences of different length must now be addressed by downstream algorithms (e.g., denoising or OTU clustering). For example, if two unique sequences differ in length by one base and have one substitution, should this count as d=1 (just the substitution) or d=2 (substitution plus terminal gap)? If large variations in length are allowed, then the phylogenetic and phenotypic resolution of the sequences may vary substantially, degrading the comparability of ZOTUs or OTUs to each other for calculating diversity, predicting taxonomy and so on. These problems are avoided by ensuring that reads of the same template sequence have the same length (global trimming, implying that reads of the same template should be globally alignable, though more distantly related sequences need not be).

Methods for global trimming

The simplest method for global trimming is to truncate all reads to the same length. This is not usually necessary with overlapping Illumina paired-end reads that have been merged by a paired-read assembler. In this case, the merged sequence always terminates at the reverse primer which guarantees that reads of the same template will have the same length regardless of variations in amplicon length between different species. If multiple primers were used which do not bind to the same locus, then trimming is required to ensure that reads of the same template amplified by different primers start and end at the same position in the biological sequence. Primer-binding bases should be discarded from the reads because PCR tends to induce substitutions at mismatched positions; in most cases this is easily accomplished by discarding a fixed number of bases (the primer lengths) from each end of the sequence. There is no need to explicitly match the primer sequence in order to trim it unless there are multiple primers binding to different loci.

Global trimming

The goal of global trimming is to ensure that reads from the same template have the same length. More accurately, it should ensure that sequences start and end at exactly the same position. If you don't do this, then two reads of the same biological sequence may have different lengths, and this causes problems in calculating the abundances of unique sequences.

Other way to state the goal of global trimming is that there should be no terminal gaps in an alignment of reads of the same template.

See defining unique sequence abundance for a technical discussion explaining why this step is essential.

The appropriate strategy for global trimming depends on your reads. See also global trimming for fungal ITS reads.

Paired reads which always overlap

If the read length is long enough that the longest amplicon will given an overlap of at least, say, 32 bases, then you don't need any additional trimming:  fastq_mergepairs does everything you need. Short amplicons will create "staggered" pairs which are correctly truncated during the merging.

Paired reads which sometimes or never overlap

If the read length is not long enough to get overlaps on longer amplicons, then you can't use the reverse reads. The best strategy is simply to discard the reverse reads (R2s) and make OTUs from the forward (R1) reads alone. See below under "Unpaired reads" for the appropriate trimming strategy.

Unpaired reads which never reach the reverse primer

If you have unpaired reads which never reach the reverse primer then they should be trimmed to a fixed length. If the reads are already fixed length (e.g. forward Illumina reads), then no trimming is necessary. You might choose to trim to a shorter length if the read quality is poor towards the end of the read (see fastq_eestats2 and fastx_truncate).

Unpaired reads which sometimes or always reach the reverse primer

If a read continues past the reverse primer, then it will include adapter sequence and then random junk. The adapter and junk must be discarded. It is probably also a good idea to delete the primer sequence since PCR tends to force the primer-binding locus to match the primer. Unfortunately, there is currently no easy way to do this in USEARCH. You can use search_oligodb to find the reverse primer, but you will need to write your own script to truncate the reads. If this is a real problem for you, let me know and I'll look into making a new command for you.