Individual Gene Cluster Statistics in Noisy Maps

Abstract. Identification of homologous chromosomal regions is important for understanding evolutionary processes that shape genome evolution, such as genome rearrangements and large scale duplication events. If these chromosomal regions have diverged significantly, statistical tests to determine whether observed similarities in gene content are due to history or chance are imperative. Currently available methods are typically designed for genomic data and are appropriate for whole genome analyses. Statistical methods for estimating significance when a single pair of regions is under consideration are needed. We present a new statistical method, based on generating functions, for estimating the significance of orthologous gene clusters under the null hypothesis of random gene order. Our statistics is suitable for noisy comparative maps, in which a one-toone homology mapping cannot be established. They are also designed for testing the significance of an individual gene cluster in isolat...

• Type: Conference
• Year: 2005
• Where: RECOMB
• Authors: Narayanan Raghupathy, Dannie Durand
• Keywords: Computational Biology, Individual Gene Cluster, Large Scale Duplication, Noisy Comparative Maps, RECOMB 2005