Computing the family-free DCJ similarity

Rubert, Diego P.; Hoshino, Edna Ayako; Braga, Marília D. V.; Stoye, Jens; Martinez, Fábio Henrique Viduani

doi:10.1186/s12859-018-2130-5

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Clearly, the method of choice are similarity measures that, rather than solely penalizing dissimilarity, quantify similarity. Conversely, global measures of DCJ similarity, such as those proposed by some of us [24,25] that only maximize the (weighted) number of cycles and paths in the adjacency graph, are unsuitable as well: In search of locally similar sequences, it is not sufficient to reward only similarity (then, a best local solution would always correspond to a global solution), but it is necessary to also penalize dissimilarity.…”

Section: Family Refinement Using Local Dcj Similaritymentioning

confidence: 99%

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

et al. 2020

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Background: Computationally inferred ancestral genomes play an important role in many areas of genome research. We present an improved workflow for the reconstruction from highly diverged genomes such as those of plants. Results: Our work relies on an established workflow in the reconstruction of ancestral plants, but improves several steps of this process. Instead of using gene annotations for inferring the genome content of the ancestral sequence, we identify genomic markers through a process called genome segmentation. This enables us to reconstruct the ancestral genome from hundreds of thousands of markers rather than the tens of thousands of annotated genes. We also introduce the concept of local genome rearrangement, through which we refine syntenic blocks before they are used in the reconstruction of contiguous ancestral regions. With the enhanced workflow at hand, we reconstruct the ancestral genome of eudicots, a major sub-clade of flowering plants, using whole genome sequences of five modern plants. Conclusions: Our reconstructed genome is highly detailed, yet its layout agrees well with that reported in Badouin et al. (2017). Using local genome rearrangement, not only the marker-based, but also the gene-based reconstruction of the eudicot ancestor exhibited increased genome content, evidencing the power of this novel concept.

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Section: Family Refinement Using Local Dcj Similaritymentioning

confidence: 99%

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

et al. 2020

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“…We have demonstrated FFDCJ-SIMILARITY is APX-hard and an inapproximability result by a strict (approximation-preserving) reduction from MAX-2SAT to FFDCJ-SIMILARITY [Rubert et al 2017b, Rubert et al 2018]:…”

Section: Family-free Dcj Similaritymentioning

confidence: 99%

“…BMC Bioinformatics, 2018. [Rubert et al 2018] 3. Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants.…”

Section: Publications In Journalsmentioning

confidence: 99%

Distance and Similarity Measures in Comparative Genomics

Rubert¹,

Stoye²,

Martinez³

2020

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC 2020)

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Research in comparative genomics supports the investigation of important questions in molecular biology, genetics and biomedicine. A central question in this field is the elucidation of similarities and differences between genomes by means of different measures. This summary, submitted to CTD 2020, briefly describes the main contributions, originality and impact possibilities of the thesis entitled "Distance and Similarity Measures in Comparative Genomics", by Diego P. Rubert.

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“…Given two sets of objects A and B, a matching matches each object of A (respectively B) to at least one object of B (respectively A). The matching has many uses including computational biology and pattern recognition [1][2][3]. We can represent the sets and their relations using a bipartite graph; for example one part can represent mutated gens and other part outlying gens [2].…”

Section: Introductionmentioning

confidence: 99%

An O(n^3) time algorithm for the maximum weight b-matching problem on bipartite graphs

Rajabi-Alni

Bagheri

Minaei‐Bidgoli

2020

Preprint

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A matching between two sets A and B assigns some elements of A to some elements of B. Finding the similarity between two sets of elements by advantage of the matching is widely used in computational biology. Frequently, the capacities of the elements are limited. That is, the number of the elements that can be matched to each element should not exceed a given number. We describe the first O(n 3 ) time algorithm for matching two sets of elements with limited capacities.We use bipartite graphs to model relationships between pairs of objects. Let G = (A ∪ B, E) denote an undirected bipartite graph with real edge weights and node capacities b(v). The b-matching of G matches each vertex v in A (B) to at least 1 and at most b(v) vertices in B (A). In this paper, we present an O(n 3 ) time algorithm for finding the maximum weight b-matching of G, where |A| + |B| = O(n). Our algorithm improves the previous best time complexity of O(n 3 log n) for this problem.

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Computing the family-free DCJ similarity

Cited by 5 publications

References 29 publications

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

Distance and Similarity Measures in Comparative Genomics

An O(n^3) time algorithm for the maximum weight b-matching problem on bipartite graphs

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