General Remarks. To perform a selection analysis, datamonkey.org needs a multiple alignment of at least three homologous coding nucleotide sequences. Codon based methods for estimating dN and dS can be applied to any sequence alignment, but there are several considerations to keep in mind:

1. Ideally, the alignment should represent a single gene, or protein product, sampled over multiple taxa (e.g. mammalian interferon genes), or a diverse population sample (e.g. Influenza A viruses infecting different individuals). Because comparative methods estimate relative rates of synonymous and non-synonymous substitution, substantial sequence diversity is needed for reliable inference. For example when, Suzuki and Nei applied a REL-type method to a very low divergence (1 or 2 substitutions per sequence along a star phylogeny) sample of the Human T-lymphotropic virus (HTLV), they found that the method performed poorly. Yang and colleagues have suggested that the total length of the phylogenetic tree should be at least one expected substitution per codon site, but it is impossible to give a generally valid range for desirable sequence divergence. However, sequences that are too divergent could lead to saturation, i.e. our inability to reliably infer branch lengths and substitution parameters.
2. The number of sequences in the alignment is important: too few sequences will contain too little information for meaningful inference, while too many may take too long to run. At the time of this writing, Datamonkey permits up to 150 sequences for SLAC analyses, 100 for FEL/IFEL analyses, 40 for REL and PARRIS and 25 for GA-Branch. As a rule of thumb, at least 10 sequences are needed to detect selection at a single site (SLAC/FEL/IFEL/REL) with any degree of reliability, while as few as 4 may be sufficient for alignment-wide inference (PARRIS/GA-Branch). The median number of sequences in an alignment submitted to Datamonkey is 19.
3. Comparative methods are ill suited to study certain kinds of selection. For example, they should not be applied to the detection of selective sweeps (rapid replacement of one allele with a more fit one, resulting in a homogeneous population), unless sequences sampled prior to and following the selective sweep are included in the sample. A number of publications have dealt with this issue extensively (e.g. Selection using HyPhy), and we refer an interested reader to one of these works for further insight.

It is a good practice to visually inspect your data to make sure that the sequences are alignment correctly. Of course, one can never be sure that an alignment is objectively ÒcorrectÓ, but gross misalignments (e.g. sequences that are out of frame) are easy to spot with software that provides a graphical visualization of the alignment, such as HyPhy, Se-Al, or BioEdit. Datamonkey uses the HyPhy package as its processing engine, and if an alignment does not open in HyPhy on your machine (using the File:Open:Open Data File command), then it will not be properly read by Datamonkey.

You should verify that the alignment is in frame, i.e. that it does not contain stop codons, including premature stop codons (indicative of a frame shift, e.g. due to misalignment, or a non-functional coding sequence) and the terminal stop codon. Your alignment should exclude any non-coding region of the nucleotide sequence, such as introns or promoter regions, for which existing models of codon substitution would not apply. When coding nucleotide sequences are aligned directly, frameshifting (i.e. not in multiples of 3) gaps may be inserted, since the alignment program often does not take the coding nature of the sequence into account. Therefore it is generally a good idea to align translated protein sequences and then map them back onto constituent nucleotides. Datamonkey will perform a number of checks when it receives coding sequences and report all problems it encounters.

If the alignment contains identical sequences, Datamonkey will discard all but one copy before proceeding. This is done to speed up the analyses, because identical sequences do not contribute any information to the likelihood inference procedure (except via base frequencies), but the computational complexity of phylogenetic analyses grows with the number of sequences.

Finally, Datamonkey may rename some of the sequences to conform to HyPhy naming conventions for technical reasons (all sequence names must be valid identifiers, e.g. they cannot contain spaces). This is done automatically and has no effect on the subsequent analyses.