A practical study of the proportion of non-unique neighbor-joining trees of microsatellite markers

Neighbor-joining (NJ) is a distance-based method used to analyse and visualise data that generates a phylogenetic tree. It is found in a wide range of scientific research and is upstream of all fields of bioinformatics and computational biology. NJ’s drawback is the generation of different binary phylogenetic trees if more than two clusters of elements have the same distance in any stage of the phylogenetic tree generation process. Therefore, articles that show only one phylogenetic tree, although NJ could generate several of them, are somewhat biased and hard to reproduce. The objective of this practical study is to quantify the extent of this problem in the literature in a particular context: the clustering of microsatellite markers with NJ. For this purpose, we calculate the proportion of publications in the Scopus database where the possibility of more than one binary phylogenetic tree exists. We have observed that 13% of articles can produce more than one binary phylogenetic tree; thus, results shown in these articles would have to be analysed or their conclusions revised. Moreover, this percentage is large enough to be considered a general problem in our scientific community. To exemplify the issue practically, we have selected an example where more than one binary phylogenetic tree solution is possible. • NJ is usually used to generate phylogenetic trees in Microsatellite data. • NJ can generate non-unique phylogenetic trees. • 13% of the papers that use NJ have non-unique phylogenetic trees. • Non-unique phylogenetic trees can have biased conclusions and be non-reproducible.