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Data Analysis

Welcome to our online implementation of Position Sensitive P-Mer Frequency Clustering, a new approach to rapidly classify 16S sequences.

This is the Data Analysis section where you can build and visualize models. The steps are:

  1. Choose Data: Here you can choose an example data set or upload your own data in FASTA format.
  2. Choose Parameters: Here you can choose a dissimilarity measure and the proximity threshold for clustering. Euclidean is a good starting point as the dissimilarity measure. A reasonable threshold is suggested after you choose a dissimilarity measure. A smaller threshold created a more detailed model with more clusters.
  3. Explore Model: After creating the model you can explore the model with a cluster similarity plot, a conservation plot or generate a report which contains the plots and more information.

Choose Data

Example Data:


Choose Parameters

Selected Data:

Dissimilarity Measure:


Explore Model

Please finish form and press generate to view models.
Please finish form and press generate to view models.