Thus, potentially unique codes can be identified even for organisms that are taxonomically divergent from known species, provided that homologous domains are found.
In this way, proteins may still be identified even if the corresponding gene model from A. flavus would be mis-annoted, e.g. by incorrect intron-exon boundaries or mis-annotated C- or N-termini.
In GV, many non-venom toxic proteins were identified even though manually milking of the venom from the gland was performed carefully.
Subtle differences were identified even among the large hydrocarbon flames and became evident from measurements of the instantaneous luminosity field.
Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGFβ, stress, and Fas pathways) can be correctly identified even from limited datasets.
Additionally, using PCR species-specific primers, all of the legume-nodulating Burkholderia were correctly identified, even from single nodules collected from inoculated common bean plants.
Thus with the help of this clique-finding algorithm, clusters can now be quickly and automatically identified even from very large BLAST reports.
Alternatively, distantly related individuals can be identified from multilocus sharing of even quite small regions of the genome (Browning and Browning 2011, 2012, 2013).
We developed a new computational analysis method showing that 1. causal genes can be identified with high probability from even a modest number of mutant genomes; 2. costs can be cut many-fold compared with a conventional genome sequencing approach via an optimized strategy of library-pooling (multiple strains per library) and tag-pooling (multiple tagged libraries per sequencing lane).
It is clear to see from Figure 7(b) that unknown parameters,, and can be identified with high accuracy even in the presence of large random noise.
