De novo chimera filtering with vsearch.
Apply the vsearch uchime_denovo method to identify chimeric feature
sequences. The results of this method can be used to filter chimeric
features from the corresponding feature table. For additional details,
please refer to the vsearch documentation.
Parameters
- sequences : FeatureData[Sequence]
- The feature sequences to be chimera-checked.
- table : FeatureTable[Frequency]
- Feature table (used for computing total feature abundances).
- dn : Float % Range(0.0, None), optional
- No vote pseudo-count, corresponding to the parameter n in the chimera
scoring function.
- mindiffs : Int % Range(1, None), optional
- Minimum number of differences per segment.
- mindiv : Float % Range(0.0, None), optional
- Minimum divergence from closest parent.
- minh : Float % Range(0.0, 1.0, inclusive_end=True), optional
- Minimum score (h). Increasing this value tends to reduce the number of
false positives and to decrease sensitivity.
- xn : Float % Range(1.0, None, inclusive_start=False), optional
- No vote weight, corresponding to the parameter beta in the scoring
function.
Returns
- chimeras : FeatureData[Sequence]
- The chimeric sequences.
- nonchimeras : FeatureData[Sequence]
- The non-chimeric sequences.
- stats : UchimeStats
- Summary statistics from chimera checking.