MOTIF-EM, on the other hand, does not do any reduction of input maps and works on their full form and is fully automated (i.e. does not require the user to guess and provide input parameters).
The map aligner expects global similarity between the two maps and works well if the two maps to be compared are very similar – alignments can be made between optical maps against other optical maps, in silico maps derived from sequence, and consensus maps derived from contigs constructed by the map assembler from optical maps.
Then you look at it on the map, and work out if you've gone the correct route.
"Knowledge of the world was developed as the children made maps and worked with clay to make lamps".
CLAM consists of four "parts": retinotopic areas, feature maps, spatial maps, and working memory.
Well that's not strictly true, my producer John Campbell and I, actually started yesterday afternoon plotting a route using online maps and working out the distances between the charge points.
I was able to utilize my degree in Environmental Geoscience, make maps, and work with an incredibly motivated group of people, mostly women, on an Army installation.
The applications page makes no mention of it, but as an aside pictures taken with the new iPhone can also be geotagged so that you can sync your photo library with a map and work out where each of the shots were taken.
The resulting 18 modules (Methods, Table 2, Fig. 4a, Additional file 2) are fully based on the underlying detailed map and help to comprehend higher order map structure, navigate through the map and work on functional modules.
You may want to begin by giving yourself a list of items to place on the map and work towards filling out the map purely from your memory.
