Adiga and his colleagues tested the new algorithm by using it to pick images from among over 130,000 ribosome particles in 55 micrographs provided by the Wadsworth Center of the New York State Department of Health in Albany. Adiga separately inspected the 55 micrographs by eye and "manually" selected particles, well over 80 percent of which turned out to be the same as those picked by the program. Fewer than 10 percent of the images chosen by the program were false positives.
A coauthor of the paper, William Baxter, independently inspected 14 of the same micrographs, chosen at random. On his first pass, intending to select only particles of the highest quality -- a "gold standard" -- he chose roughly two-thirds of the same particles picked by the software. When the program's additional candidates were inspected more closely, however, many turned out to be true positives of good quality; only about 10 percent of the program's picks were false positives.
Similar results were obtained when the segmentation program was used to pick particles from a smaller and more difficult molecule, a convex or "boat-shaped" enzyme labeled TPP-II, isolated from the fruit-fly. Although an initial comparison between manual selection and automatic selection indicated that 15 percent of the program's nominations were false positives, when the program was run again -- using a template after segmentation to filter out incompatible shapes -- false positives dropped to a mere 7 percent.
Beyond the demonstrated goal of selecting the same particles an expert would select with a low error rate, future refinement of the segmentation algorithm aims higher. By concentrating on the highest quality particles, crystallization in silico may need far fewer than hundreds of thousands of particles.
"Jacqueline Milne of the National Cancer Institute has demonstrated that high-quality structural maps can be achieved with a few hundred parti
Source:DOE/Lawrence Berkeley National Laboratory