In this way, GeneScissors is able to leverage statistics of fragment distribution and other features of the alignments.
Another important feature of the alignments is linked to the distribution of the conserved blocks.
Features of the conserved alignment included N-terminal Somatomedin-B domain(s), inactive pro-cathepsin B-like domains and the signature lipocalin domain.
Both measure the folding energy differences of the individual sequences, but do not capture identical features of the input alignment nor can be transformed into each other.
The pseudocharacter matrix is contained in Additional File 2. The features of the inferred alignments and ML trees are shown in Table 2. Considerable differences regarding alignment length, estimated alpha values of the gamma distribution and highest obtained likelihood values were observed.
In this section, we describe the general features of the resulting alignments.
Instead, RAF defines a fixed set of basis features describing aspects of the alignment, RNA secondary structure, or both.
We derive the position-specific features from the alignment of the sequence homologs of a given protein while the context-specific features include amino acid identify, hydropathy index, both 3-class and 8-class secondary structure, and solvent accessibility.
The most prominent feature of the resulting alignment is the presence of several highly conserved blocks of amino acids separated by species- and protein-specific insertions.
The most commonly used method for partitioning alignments, and the only one currently suited to very large datasets, is to define subsets according to structural features of the sequences in the alignment, such as gene boundaries, codon positions, structural components of rRNAs (such as stems and loops), or some combination of these.
