Comparison of clone-ordering algorithms used in physical mapping

An algorithm for clustering cDNA fingerprints

Clustering large data sets is a central challenge in gene expression analysis. The hybridization of synthetic oligonucleotides to arrayed cDNAs yields a fingerprint for each cDNA clone. Cluster analysis of these fingerprints can identify clones corresponding to the same gene. We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. Unlike other methods, it does not assume that the clusters are hierarchically structured and does not require prior knowledge on the number of clusters. In tests with simulated libraries the algorithm outperformed the Greedy method and demonstrated high speed and robustness to high error rate. Good solution quality was also obtained in a blind test on real cDNA fingerprints.

Construction of physical maps from oligonucleotide fingerprints data

A new algorithm for the construction of physical maps from hybridization fingerprints of short oligonucleotide probes has been developed. Extensive simulations in high-noise scenarios show that the algorithm produces an essentially completely correct map in over 95% of trials. Tests for the influence of specific experimental parameters demonstrate that the algorithm is robust to both false positive and false negative experimental errors. The algorithm was also tested in simulations using real DNA sequences of C. elegans, E. coli, S. cerevisiae, and H. sapiens. To overcome the non-randomness of probe frequencies in these sequences, probes were preselected based on sequence statistics and a screening process of the hybridization data was developed. With these modifications, the algorithm produced very encouraging results.