size ranges can easily be pooled and loaded in a single capillary. However, the resulting complexity of the pooling schemes and the amount of data generated require an automated workflow for acquisition and processing of fragment files. Moreover, it is well-known that fragment sizes, compared between two genotyping laboratories using capillary electrophoresis (CE), can differ considerably because different laboratories may use different CE machines and running conditions. Methods: Data processing: The software handles any pooling strategy (combination of dyes and size-compatible VNTRs) which can be parsed from the file name. Thus, each VNTR is defined by a pool, dye and expected size range, defined by the repeat length, offset and expected copy range. Using that information the software will automatically screen (in batch) all theoretical ranges for each VNTR and report on the peak(s) found (or not) in those ranges. Results: The resulting VNTR information is stored in integer-type character sets where each VNTR represents one character. For comparisons within the same laboratory or between laboratories with compatible instruments and procedures, the software allows the user to handle calculations based on an expected band size range, depending on the known offset, repeat length, expected copy number variation and a user-defined tolerance. In order to deal with a possible experimental size shift linked to the CE system used, a custom ‘mapping’ tool was developed, allowing observed sizes for a specific instrument to be mapped to real sizes and exact copy numbers. Conclusion: Data analysis: VNTR data can be analyzed as categorical characters (each different copy number is a different allele) or as quantitative characters. In the latter case, the larger the difference between copy numbers, the less related the organisms are considered. The Minimum Spanning Tree algorithm applied on VNTR data in BioNumerics has proven to be extremely useful for epidemiological study and population genetics.