As for aligning reads from repetitive regions, our simulated data show that reads from repetitive regions tend to be aligned incorrectly, and suppressing reads with multiple hits can improve alignment accuracy.
As for aligning reads from repetitive regions, our simulation data show that reads from repetitive regions tend to be aligned incorrectly, and suppressing reads with multiple hits can improve alignment accuracy.
Particle pairs with alignment errors larger than three times the reported precision were considered as aligned incorrectly.
This is illustrated in Figure 2. Promiscuous domains present a significant challenge to orthology prediction: all methods of orthology prediction that are based either implicitly (as in graph-based methods) or explicitly (as in domain-based phylogenetic methods) on local alignment can incorrectly cluster proteins with different domain architectures into orthology groups.
For these sequences, the default version of DIALIGN produces serious mis-alignments where entire genes are incorrectly aligned, but meaningful alignments can be obtained if the known gene boundaries are used as anchor points.
Furthermore, the ungapped segments of the structure alignment were placed incorrectly.
Splign can report both sense and anti-sense alignments for a single mRNA which may give it an advantage over the tools that report alignments in one direction, because an incorrectly directed alignment can have an identity higher than the one in the correct direction.
In fact, this region was only detected due to the presence of a previously unannotated and weakly conserved adjacent region, notated as bsAlt, which is incorrectly aligned in the multiz28 way alignment.
The new TarB ORFs in Mu50 and N315 strains and their alignment with the original incorrectly predicted ones are shown in Figure 3a.
Inferring an accurate phylogeny required generating: 1) A well annotated data set across species based on genome synteny; 2) Alignments with unaligned or incorrectly overaligned sequences filtered out; and 3) Diverse data sets, including genes and their inferred trees, indels, and transposable elements.
