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11-Aug-2018 New paper describes octave plots for visualizing alpha diversity.

12-Jun-2018 New paper shows that one in five taxonomy annotations in SILVA and Greengenes are wrong.

18-Apr-2018 New paper shows that taxonomy prediction accuracy is <50% for V4 sequences.

05-Oct-2017 PeerJ paper shows low accuracy of closed- and open-ref. QIIME OTUs.

22-Sep-2017 New paper shows 97% threshold is wrong, OTUs should be 99% full-length 16S, 100% for V4.

24-Nov-2016
UPARSE tutorial video posted on YouTube. Make OTUs from MiSeq reads.

 

USEARCH v11

Defining unique sequence abundances

See also
 
Global trimming

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.