Node alignment example using Algorithm 1 in Section S3.1.
To compare the alignment accuracy of different systems, the training images, which are used to train neural networks, and the testing images, which are used to check the alignment accuracy, are generated by the way described in Table 5. Figure 13 depicts an alignment example for a testing image on three different systems.
We have already examined the results of the first control condition, the case where <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0008778.e130.PNG" class= inline-graphic"/>, in which we see good pinwheel maps form in each barrel but no consistent global alignment (example in Fig. 6a).
This sequence set consists of four DNA-binding proteins and is a challenging alignment example as there is only weak similarity at the primary sequence level.
Thus, in cases where it is desirable to query low %_PERF_ALIGN values, we developed a further index, "%_STOP_DIST", (Table 1) allowing to assess the possible presence of a stop in the alignment (see examples in Table 1).
Figure 24 (a) The Carter dataset: (a) global picture, (b) and (c) two successfully aligned views, (d) an alignment failure example.
One approach could be to implement model partitioning by inferring among-site variation from the alignment, for example, using a mixture model in the context of a Bayesian framework, such as that developed by Lartillot and Philippe (2004) in PhyloBayes, where each site in the alignment falls into one of several classes characterized by its own set of frequencies (CAT model).
In the case of your sales team, rather than focusing on alignment, for example, set a different goal.
Thus when three nucleic acid sequences are aligned there are 4 = 64 possible patterns at each position of the alignment, for example, (AAA, AAG, …).
The method presented here will benefit from extrinsic genomic information "addons" for boosting performance via use of homology matching, or EST alignment, for example.
