Sentence examples for identifying gis from inspiring English sources

We used three GI prediction methods that were used previously for other analyses of GIs and are considered effective methods for identifying GIs on a high-throughput scale [17], [18].

Known methods for identifying GIs are primarily based on GC contents of the islands, their oligo-nucleotide usage patterns and the codon usage biases in the genes present in the island [ 10, 12, 13, 16].

Recently Vernikos and Parkhill [ 18] presented an interesting machine learning approach for identifying GIs in bacteria based upon weights of eight criteria, including: the interpolated variable order motif (IVOM) score, presence/absence of integrase, presence/absence of phage-related protein domains, size, RNA, density, repeats, and insertion points.

On the other hand it may identify GIs with such different composition values that a compositionally similar donor is unlikely.

Firstly, key ecological patches were identified by GIS and RS methods.

The distribution patterns of various types of erosion were identified by GIS overlay of buffer strips and soil erosion maps.

The spatial locations and trends of disease outbreaks can be identified by GIS [ 23].

There were four very large parks in the sample (> 200 000 m) that were all identified by GIS.

A secondary finding regarding the large number of parks that were not identified by GIS warrants a brief discussion.

There were no systematic differences between the types of parks identified via GIS or on-site, other than size.

For example, we assessed whether morphological and physiological trait variation within and between two wild tomato species (assessed under common garden and drought manipulation conditions) matched climatic variation identified with GIS analyses (Nakazato et al. 2008).