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See also
UNCROSS algorithm
  UNCROSS paper
  uncross command
  Cross-talk example (GAIIx)
  Cross-talk example (MiSeq)

Cross-talk errors assign reads to incorrect samples
In marker gene amplicon sequencing, samples are often multiplexed into a single run by embedding index sequences into amplicons to identify the sample of origin. Reads are assigned to samples (demultiplexed) according to their index sequences. A cross-talk error occurs when a read is assigned to an incorrect sample.

Illumina has a ~2% cross-talk error rate
The cross-talk error rate was estimated to be ~2% in twelve Illumina datasets including one single-indexed GAIIx run and eleven dual-indexed MiSeq runs, as described in the UNCROSS paper. In a given OTU, the number of reads assigned to a single sample could be inflated by up to ~0.5% of the total reads in that OTU. Thus, if the OTU table shows that up to around 0.5% of the reads were assigned to a given sample,  the correct count could be zero and this would then give a false-positive identification of the species (or group of species) in the OTU. Cross-talk thus tends to inflate estimates of richness and alpha diversity. Beta diversity may also be inflated because samples may appear to share the same spurious OTUs.

Filtering cross-talk
The UNCROSS algorithm uses simple heuristics that attempt to identify and filter cross-talk in an OTU table. Cross-talk can be identified most reliably in control samples such as a null sample (e.g. distilled water) and designed (mock) communities where the sequences are known.