USEARCH manual > algorithms > UCHIME
UCHIME score
 
Score options
Command-line options for the UCHIME scoring function are shown in the following table; see below for explanation.
 
Option   Default Description
‑minh   0.28 Minimum score (h) to be classified as chimera. Increasing this value tends to increase the number of false positives (and also sensitivity).
‑xn   8.0 Weight of "no" vote (β).  Increasing this value tends to decrease the number of false positives (and also sensitivity). Must be > 1.
‑dn   1.4 Pseudo-count prior for "no" votes. (n). Increasing this value tends to decrease the number of false positives (and also sensitivity). Must be > 0.
‑mindiffs   3 Minimum number of diffs in a segment. Increasing this value tends to reduce the number of false positives while reducing sensitivity to very low-divergence chimeras. Must be > 0.
‑mindiv   0.8 Minimum divergence, i.e. 100% - identity between the query and closest reference database sequence. Expressed as a percentage, so the default is 0.8%, which allows chimeras that are up to 99.2% similar to a reference sequence. This value is chosen to improve sensitivity to very low-divergence chimeras as needed to achieve a high score on the SIM2 benchmark (see UCHIME paper). I generally recommend increasing this value to, say, 1.5 to further reduce the number of false positives, which allows reducing the ‑minh option to improve sensitivity to higher-divergence chimeras. Must be > 0.

UCHIME alignment
A typical UCHIME alignment. The query sequence is Q, the putative parent sequences are A and B. The true parent sequences may not be present in the reference database, in which case a closely related sequence ("step-parent") might be used instead.

Diffs and votes
In a typical alignment, most columns are identities q=a=b, where q, a and b are letters from Q, A and B respectively. A column in which at least one sequence differs from the other two is called a diff. Diffs can be considered as votes for or against the model. For example, a diff q=a, q≠b increases the distance d(Q,B) while leaving d(Q,A) unchanged. If such a diff is found in the segment that is closer to A, it can be regarded as a "yes" vote supporting the model; if it is found in the segment that is closer to B then it contradicts the model and is regarded as a "no" vote. A diff in which all three sequences differ or in which a=b, q≠a, q≠b increases the distance of Q to both A and B and is regarded as an "abstain" vote that neither supports nor contradicts the model. Let Yg, Ng and Ag be the total number of yes, no and abstain votes in segment g of the model, where g is L (left) or R (right). If YL > NL > NL and YR > NR, the alignment is chimeric and the model is closer to Q than A or B alone. The number of diffs may be very small in more challenging cases. For example, in a 16S experiment using 200nt reads, clusters of radius ~3% might be used in an attempt to identify species. It would then be important to identify chimeras with divergences as low as ~2%, which could have a few as four diffs with their closest parents. In such cases, the small amount of evidence available should increase the uncertainty of the classification. The ‑mindiffs option sets the minimum number of diffs that must be present in a segment; increasing this value tends to reduce the number of false positives while reducing sensitivity to very low-divergence chimeras.

UCHIME scoring function
Each segment g (left and right of the cross-over) is assigned a score:

Hg = Yg / (β (Ng + n) + Ag).

Intuitively, this can be understood as a generalization of the ratio Y/N, which must be >1 for the alignment to be chimeric. The β parameter (-xn option, which should be >1) gives a no vote a higher weight than a yes vote, and the n parameter (-dn option, which should be >0 and is set to 1.4 by default) acts as a pseudo-count prior on the number of no votes. A positive value of n reduces H, especially when Y is small; this models increased uncertainty with reduced evidence. Abstain votes also lower the score as they indicate noise or the use of a step-parent, either of which should increase uncertainty. The query is classified as a chimera if:

H = HL x HR ≥ h.

Here, h is the minimum score threshold (-minh option).