Figure 1.
Overview of the components constituting the FSA alignment program.
The algorithms that are used in each component are highlighted in the accompanying boxes. The bold arrows show the simplest mode of use for FSA, where posterior probabilities are calculated directly using default parameters for all pairs of sequences and the optional steps of anchor finding and iterative refinement are omitted.
Figure 2.
The default Pair HMM used by FSA.
By default FSA uses a Pair HMM with two sets of Insert (I) and Delete (D) states to generate a two-component geometric mixture distribution. FSA can optionally use a three-state HMM, which has only one set of Insert and Delete states. M is a Match state emitting aligned characters.
Figure 3.
Two alignments (left and right) which make the same homology statements and therefore are both represented by the same POSET (center).
“The mathematics of distance-based alignment” in Text S1 discusses this view of alignments as POSETs. The alignment on the right minimizes the number of gap-open events and as such is appropriate for analyses such as inferring parsimonious indel frequencies across a clade. Alignments are displayed with TeXshade [63].
Figure 4.
Schematic overview of FSA's parallelization strategy on a computer cluster.
For large input sizes, a disk-based database may be used to store some of the primary data structures and reduce memory usage.
Figure 5.
The Java GUI allows users to visualize the estimated alignment accuracy under FSA's statistical model.
FSA's alignment is colored according the expected accuracy under FSA's statistical model (top) as well as according to the “true” accuracy (bottom) given from a comparison between FSA's alignment and the reference structural alignment. It is clear from inspection that accuracies estimated under FSA's statistical model correspond closely to the true accuracies. Sequences are from alignment BBS12030 in the RV12 dataset of BAliBASE 3 [24].
Table 1.
Benchmarks against protein structural databases.
Table 2.
Benchmarks against RNA structural databases.
Table 3.
Benchmarks against simulated mammalian and fly genomic DNA.
Table 4.
Benchmarks against simulated unrelated protein and DNA sequences.
Table 5.
Benchmarks against simulated unrelated genomic DNA.
Table 6.
Comparisons of alignments obtained in codon and amino acid space.
Table 7.
Ablation analysis of FSA on protein structural databases.
Table 8.
Ablation analysis of FSA on RNA structural databases.
Table 9.
Ablation analysis of FSA on simulated mammalian genomic DNA.
Table 10.
Ablation analysis of FSA on simulated unrelated protein and DNA sequences.
Table 11.
Timing comparison of FSA and other methods on 16S sequences.
Table 12.
Timing comparison of FSA in regular and parallelized modes.
Table 13.
Timing comparison of FSA in parallelized mode with different numbers of processors.