It should also be noted that although a rather relaxed E-value (0.0001) cut-off was used, the observed E-values between the test sequence and the closest query sequence were much below this threshold indicating that the hits obtained were highly significant.
The goal here would be to achieve a close similarity between the query and target profiles by manipulating the cIFN sequence.
Of primary interest, however, are genetic interactions that occur within the same biological process because they indicate a close functional relationship between query and target gene.
This supports the use of a Conditional Stepped Reciprocal Best Hit (CSRBH) approach, which uses whichever species is a closer match to the query and performs the appropriate ortholog prediction route (figure 1).
In order to be able to use "best close match", we first determined the optimal threshold to be 0.05 K2P distance and this value was used to decide whether a query had a close enough barcode match be given a "positive" assignment.
Putative horizontally transferred genes (HTGs; defined as genes whose greatest homology, based on BLASTP scores, is to genes from a more distant phylogenetic group than to genes from the same or a close phylogenetic group as the query genome) were compiled using the integrated microbial genomes (IMG) system http://img.jgi.doe.gov.gov
Our approach to the verification problem is based on showing a close relationship to the problem of query containment, studied in the database literature.
Similarly, good sequence identity, with a small EBLASTp[query vs PDB] value indicates that the query sequence has a close homologue in the PDB structural database.
This works well if the query genome is a close relative of genomes in the database, and the speed of the RAST server (Rapid Annotations using Subsystems Technology [ 35]) in part depends on this approach.
Using the conventional Hausdorff distance metric [14] as the image feature matching technique, the maximum local distance is chosen as the matching distance, which produces low distance even if every pair of image feature elements are close between a query image and a dataset image.
Proximity searching is the problem of retrieving, from a given database, those objects closest to a query.
