# Global Repeat Map (GRM) Application: Finding All DNA Tandem Repeat Units
## 4 Discussion  
In this study, we present our new GRM application based on the ideas of Key string and the GRM diagram that we introduced earlier in Refs. [50; 64; 65]. As a case study, using the GRM application, we analyze whole human, chimpanzee and mouse chromosome 19. In these chromosomes, we identify over 10,000 different arrays of TRs and HORs and analyze three characteristic examples from the human chromosome, which indicate substantial differences between DNA TR organization in the human, chimpanzee and mouse genome. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TR Unit (bp)** & **No. Copies** & **Start (bp)** & **End (bp)** & **CG(\%)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 2: TRs from human chromosome 19 centromeric region. The data are result of the GRM application and are part of the analysis given in Supplementary Table S1. Some of \(\sim\)171 bp alpha satellite arrays in table are additionally organized in HORs as can be concluded from Figure 6 (left side of Fragment diagram). Figure 7: Distribution of repetitive sequences over TR unit length in human (**a**), chimpanzee (**b**) and mouse (**c**) chromosome 19. (**a–c**) The range of the abscissa is limited to TRs of length 400 bp for better visibility. (**d**) Radar chart with the proportion of bases in TR, divided into five groups according to the length of the basic TR unit (0–50 bp, 51–100 bp, 101–150 bp, 151–200 bp and >200 bp). Human 340 bp TRs are in most cases alpha satellite 2 mer HORs with basic \(\sim\)171 bp repetitive monomer. Let us comment on the comparison with other known computer tools for the identification and analyses of TRs [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Unlike all the listed algorithms, the GRM application can identify all types of TRs, from the smallest (2 bp TR arrays) to the very large, as large as tens of kilobasepairs. It is very important to emphasize that the GRM application, unlike most other TR algorithms, does not require any prior knowledge of sequence structure and does not require any user-defined parameters or thresholds. In this way, it ensures that the full spectrum of TRs can be detected in just one step and that results do not depend on any initial input. Furthermore, it is robust to all types of mutations in TR copies and, therefore, can identify TRs with various complexities in the sequence pattern. In addition, the GRM application offers additional GUI tools: (i) the GRM diagram, which can be used to analyze the internal structure of each TR and to discover possible HOR structures within it; and (ii) the Sequence report that displays TR sequences as a series of basic repetitive units with colored nucleotides. In this way, the Sequence report can be used for the visual and easy identification of all kinds of indels (insertions, deletions and substitutions). Furthermore, this representation of TRs over a series of individual copies can easily be used for the additional statistical analysis of the differences between them or to search for a consensus sequence. Finally, the GRM application is very fast, efficient and simple from a computational viewpoint, with powerful graphical user interface applications. It provides a fast scan of TRs in a whole chromosome sequence, taking 1-2 min computing time per average chromosome using MacBook with a 1.6 GHz Dual-Core Intel Core i5 processor. Next, let us comment on the possible flaws of the GRM application. The weakest point of this algorithm is the part of the third step (Algorithm outline), in which we divide the resulting array into smaller subarrays of variable length. As we explain in the Algorithm outline, we started with a size of 100 bp for each subarray, calculated a GRM diagram, and increased the size of a subarray as long as only the peaks of one TR and its multiples were present. As the upper limit of this procedure, we took the length of 2000 bp.