Removing Noise and Ambiguities from Comparative Maps in Rearrangement Analysis

Abstract

Abstract-Comparison of genomic maps is hampered by errors and ambiguities introduced by mapping technology, incorrectly resolved paralogy, small samples of markers, and extensive genome rearrangement. We design an analysis to remove or resolve most of these problems and to extract corrected data where markers occur in consecutive strips in both genomes. To do this, we introduce the notion of prestrip, an efficient way of generating these and a compatibility analysis culminating in a Maximum Weighted Clique (MWC) search. The output can be directly analyzed with genome rearrangement algorithms, allowing the restoration of some of the data not incorporated into the clique solution. We investigate the trade-off between criteria for discarding excessive prestrips to make MWC feasible in terms of retaining as many markers as possible in the solution and producing an economical rearrangement analysis. We explore these questions through simulation and through comparison of the rice and sorghum genomes.

Parameterized Algorithms in Bioinformatics: An Overview

“…Problem Description. In the MAXIMAL STRIP RECOVERY problem [33] (see Figure 4), the goal is again to identify common blocks between two genomes. But here, we may remove characters (genes) from the input strings so that the resulting subsequences can be partitioned into common strips.…”

A new parallel tabu search algorithm for the optimization of the maximum vertex weight clique problem

“…MVWCP is a maximization problem that tries to find a clique in G with a maximum w . Bioinformatics, 6 coding theory, 7 protein structure prediction, 8 computer vision, 9 combinatorial auctions, 10 cluster detection 11 are some of the areas that MVWCP has been applied. Due to the intractable nature of MVWCP, its large problem instances cannot be solved to the best value in reasonable execution times.…”