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A Graph-Theoretical Approach to the Selection of the Minimum Tiling Path from a Physical Map
March-April 2013 (vol. 10 no. 2)
pp. 352-360
Serdar Bozdag, Dept. of Math., Stat. & Comput. Sci., Marquette Univ., Milwaukee, WI, USA
Timothy J. Close, Dept. of Botany & Plant Sci., Univ. of California, Riverside, Riverside, CA, USA
Stefano Lonardi, Dept. of Comput. Sci. & Eng., Univ. of California, Riverside, Riverside, CA, USA
The problem of computing the minimum tiling path (MTP) from a set of clones arranged in a physical map is a cornerstone of hierarchical (clone-by-clone) genome sequencing projects. We formulate this problem in a graph theoretical framework, and then solve by a combination of minimum hitting set and minimum spanning tree algorithms. The tool implementing this strategy, called FMTP, shows improved performance compared to the widely used software FPC. When we execute FMTP and FPC on the same physical map, the MTP produced by FMTP covers a higher portion of the genome, and uses a smaller number of clones. For instance, on the rice genome the MTP produced by our tool would reduce by about 11 percent the cost of a clone-by-clone sequencing project. Source code, benchmark data sets, and documentation of FMTP are freely available at under MIT license.
Index Terms:
software packages,biological techniques,biology computing,genomics,graph-theoretical approach,Fingerprint-based MTP software,FingerPrinted Contigs software,FMTP documentation,FMTP benchmark data set,FMTP source code,clone-by-clone sequencing project,rice genome,MTP production,FPC software,FMTP software,minimum spanning tree algorithm,minimum hitting set algorithm,clone-by-clone genome sequencing project,hierarchical genome sequencing project,MTP computation,physical map,minimum tiling path selection,Physical mapping,Minimum tiling path,FMTP,FPC,Minimum hitting set,minimum tiling path,physical mapping
Serdar Bozdag, Timothy J. Close, Stefano Lonardi, "A Graph-Theoretical Approach to the Selection of the Minimum Tiling Path from a Physical Map," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 352-360, March-April 2013, doi:10.1109/TCBB.2013.26
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