1 Department of Mathematics and Computer Science (IMADA), Faculty of Science, SDU2 Computer Science, Department of Mathematics and Computer Science (IMADA), Faculty of Science, SDU3 unknown4 Department of Mathematics and Computer Science (IMADA), Faculty of Science, SDU
Homology detection is a long-standing challenge in computational biology. To tackle this problem, typically all-versus-all BLAST results are coupled with data partitioning approaches resulting in clusters of putative homologous proteins. One of the main problems, however, has been widely neglected: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting clusters of homologous proteins between a huge set of species would open opportunities for better understanding the genetic repertoire of bacteria with different lifestyles.