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151
A Very Elementary Presentation of the HannenhalliPevzner Theory
, 2003
"... In 1995, Hannenhalli and Pevzner gave a first polynomial solution to the problem of finding the minimum number of reversals needed to sort a signed permutation. Their solution, as well as subsequent ones, relies on many intermediary constructions, such as simulations with permutations on 2n elem ..."
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Cited by 78 (9 self)
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In 1995, Hannenhalli and Pevzner gave a first polynomial solution to the problem of finding the minimum number of reversals needed to sort a signed permutation. Their solution, as well as subsequent ones, relies on many intermediary constructions, such as simulations with permutations on 2n elements, and manipulation of various graphs.
GRIMM: genome rearrangements web server
 Bioinformatics
, 2002
"... Summary: GRIMM is a tool for analyzing rearrangements of gene orders in pairs of unichromosomal and multichromosomal genomes, with either signed or unsigned gene data. Although there are several programs for analyzing rearrangements in unichromosomal genomes, this is the ¯rst to analyze rearrangem ..."
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Cited by 77 (5 self)
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Summary: GRIMM is a tool for analyzing rearrangements of gene orders in pairs of unichromosomal and multichromosomal genomes, with either signed or unsigned gene data. Although there are several programs for analyzing rearrangements in unichromosomal genomes, this is the ¯rst to analyze rearrangements in multichromosomal genomes. GRIMM also provides a new algorithm for analyzing comparative maps for which gene directions are unknown. Availability: A web server, with instructions and sample data, is available at
Efficient algorithms for multichromosomal genome rearrangements
 Journal of Computer and System Sciences
"... Abstract. Hannenhalli and Pevzner [5] gave a polynomial time algorithm for computing the minimum number of reversals, translocations, fissions, and fusions, that would transform one multichromosomal genome to another when both have the same set of genes without repeats. We fixed some problems with t ..."
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Cited by 75 (4 self)
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Abstract. Hannenhalli and Pevzner [5] gave a polynomial time algorithm for computing the minimum number of reversals, translocations, fissions, and fusions, that would transform one multichromosomal genome to another when both have the same set of genes without repeats. We fixed some problems with the construction: (1) They claim it can exhibit such a sequence of steps, but there was a gap in the construction. (2) Their construction had an asymmetry in the number of chromosomes in the two genomes, whereby forwards scenarios could have fissions but not fusions. We also improved the speed by combining the algorithm with the algorithm of Bader, Moret, and Yan [2] that produces reversal scenarios for permutations in linear time. 1.
The reconstruction of doubled genomes
 SIAM Journal on Computing
"... Abstract. The genome can be modeled as a set of strings (chromosomes) of distinguished elements called genes. Genome duplication is an important source of new gene functions and novel physiological pathways. Originally (ancestrally), a duplicated genome contains two identical copies of each chromos ..."
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Cited by 53 (15 self)
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Abstract. The genome can be modeled as a set of strings (chromosomes) of distinguished elements called genes. Genome duplication is an important source of new gene functions and novel physiological pathways. Originally (ancestrally), a duplicated genome contains two identical copies of each chromosome, but through the genomic rearrangement mutational processes of reciprocal translocation (prefix and/or suffix exchanges between chromosomes) and substring reversals, this simple doubled structure is disrupted. At the time of observation, each of the chromosomes resulting from the accumulation of rearrangements can be decomposed into a succession of conserved segments, such that each segment appears exactly twice in the genome. We present exact algorithms for reconstructing the ancestral doubled genome in linear time, minimizing the number of rearrangement mutations required to derive the observed order of genes along the presentday chromosomes. Somewhat different techniques are required for a translocationsonly model, a translocations/reversals model, both of these in the multichromosomal context (eukaryotic nuclear genomes), and a reversalsonly model for single chromosome prokaryotic and organellar genomes. We apply these methods to the yeast genome, which is thought to have doubled, and to the liverwort mitochondrial genome, whose duplicate genes are unlikely to have arisen by genome doubling.
On the Similarity of Sets of Permutations and its Applications to Genome Comparison
, 2003
"... The comparison of genomes with the same gene content relies on our ability to compare permutations, either by measuring how much they di#er, or by measuring how much they are alike. With the notable exception of the breakpoint distance, which is based on the concept of conserved adjacencies, meas ..."
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Cited by 52 (12 self)
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The comparison of genomes with the same gene content relies on our ability to compare permutations, either by measuring how much they di#er, or by measuring how much they are alike. With the notable exception of the breakpoint distance, which is based on the concept of conserved adjacencies, measures of distance do not generalize easily to sets of more than two permutations. In this paper, we present a basic unifying notion, conserved intervals, as a powerful generalization of adjacencies, and as a key feature of genome rearrangement theories. We also show that sets of conserved intervals have elegant nesting and chaining properties that allow the development of compact graphic representations, and linear time algorithms to manipulate them.
Steps Toward Accurate Reconstructions of Phylogenies from GeneOrder Data
 J. COMPUT. SYST. SCI
, 2002
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Assignment of orthologous genes via genome rearrangement
 IEEE/ACM Transactions on Computational Biology and Bioinformatics
, 2005
"... Abstract—The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not cl ..."
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Cited by 47 (4 self)
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Abstract—The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at a genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement. First, the problem is formulated as that of computing the signed reversal distance with duplicates between the two genomes of interest. Then, the problem is decomposed into two new optimization problems, called minimum common partition and maximum cycle decomposition, for which efficient heuristic algorithms are given. Following this approach, we have implemented a highthroughput system for assigning orthologs on a genome scale, called SOAR, and tested it on both simulated data and real genome sequence data. Compared to a recent ortholog assignment method based entirely on homology search (called INPARANOID), SOAR shows a marginally better performance in terms of sensitivity on the real data set because it is able to identify several correct orthologous pairs that are missed by INPARANOID. The simulation results demonstrate that SOAR, in general, performs better than the iterated exemplar algorithm in terms of computing the reversal distance and assigning correct orthologs. Index Terms—Ortholog, paralog, gene duplication, genome rearrangement, reversal, comparative genomics. 1
A 1.375Approximation Algorithm for Sorting by Transpositions
"... Sorting permutations by transpositions is an important problem in genome rearrangements. A transposition is a rearrangement operation in which a segment is cut out of the permutation and pasted in a different location. The complexity of this problem is still open and it has been a tenyearold ope ..."
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Cited by 46 (2 self)
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Sorting permutations by transpositions is an important problem in genome rearrangements. A transposition is a rearrangement operation in which a segment is cut out of the permutation and pasted in a different location. The complexity of this problem is still open and it has been a tenyearold open problem to improve the best known 1.5approximation algorithm. In this paper we provide a 1.375approximation algorithm for sorting by transpositions. The algorithm is based on a new upper bound on the diameter of 3permutations. In addition, we present some new results regarding the transposition diameter: We improve the lower bound for the transposition diameter of the symmetric group, and determine the exact transposition diameter of 2permutations and simple permutations.
BioPerf: A benchmark suite to evaluate highperformance computer architecture on bioinformatics applications
 In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC
"... The exponential growth in the amount of genomic data has spurred growing interest in large scale analysis of genetic information. Bioinformatics applications, which explore computational methods to allow researchers to sift through the massive biological data and extract useful information, are beco ..."
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Cited by 44 (5 self)
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The exponential growth in the amount of genomic data has spurred growing interest in large scale analysis of genetic information. Bioinformatics applications, which explore computational methods to allow researchers to sift through the massive biological data and extract useful information, are becoming increasingly important computer workloads. This paper presents BioPerf, a benchmark suite of representative bioinformatics applications to facilitate the design and evaluation of highperformance computer architectures for these emerging workloads. Currently, the BioPerf suite contains codes from 10 highly popular bioinformatics packages and covers the major fields of study in computational biology such as sequence comparison, phylogenetic reconstruction, protein structure prediction, and sequence homology & gene finding. We demonstrate the use of BioPerf by providing simulation points of precompiled Alpha binaries and with a performance study on IBM Power using IBM Mambo simulations crosscompared with Apple G5 executions. The BioPerf suite (available from www.bioperf.org) includes benchmark source code, input datasets of various sizes, and information for compiling and using the benchmarks. Our benchmark suite includes parallel codes where available. 1.
Reconstructing Phylogenies from GeneContent and GeneOrder Data
 MATHEMATICS OF EVOLUTION AND PHYLOGENY, OLIVIER GASCUEL (ED.)
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