Phylogenetic Reconstruction for Copy-Number Evolution Problems

Xia, Ruofan; Lin, Yu; Zhou, Jun; Geng, Tieming; Feng, Bing; Tang, Jijun

doi:10.1109/tcbb.2018.2829698

Cited by 2 publications

(5 citation statements)

References 25 publications

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“…The problem can be solved in pseudo-polynomial time O(n 2 N 7 ), where n is the CNP size and N the maximum copy number. Other distances and phylogenetic approaches are discussed in [12,10,13,11,26,27] Our results. The above CNP comparison frameworks limit events to alter copynumbers by 1 or −1.…”

Section: Introductionmentioning

confidence: 73%

“…For instance, we could consider maximum parsimony as in [11], where the objective is to minimize the number of events across branches of a tree. The recent distance-based approach of Xia et al [12], which is based on the MEDICC model but with an extra error correction step, should also be evaluated in our setting. On another note, it remains to test our approach on real data.…”

Section: Discussionmentioning

confidence: 99%

“…This question has recently led to the development of several phylogenetic algorithms tailored for cancer evolution. Most of them use either information of single nucleotide variants obtained from bulk [2,3,4,5] or single-cell [6,7,8] sequencing data, or copy-number alter-arXiv:2002.11271v1 [q-bio.GN] 26 Feb 2020 ations [9,10,11,12,13] (usually in the context of single-cell data). We refer the reader to [14] for a survey of these methods.…”

Section: Introductionmentioning

confidence: 99%

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Comparing copy-number profiles under multi-copy amplifications and deletions

Cordonnier

Lafond

2020

BMC Genomics

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Background: during cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by 1 or −1, even though these events can affect large portions of a chromosome.Results: we propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor 2 approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called cnp2cnp. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by cnp2cnp and compare it against two other alternatives, namely the MEDICC distance and the Euclidean distance.Conclusions: the experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the MEDICC distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the MEDICC approach should preferred over the Euclidean distance.

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Section: Introductionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing copy-number profiles under multi-copy amplifications and deletions

Cordonnier

Lafond

2020

BMC Genomics

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“…global best median genome will not be worse than the score of the local best median genome, the global best median genome is set as the target genome. By applying the DCJ sorting strategy, we sampled six candidate median genomes that fell on the DCJ sorting path with the following: 1 10 d DCJ (Pbest, pbest) , 2 10 d DCJ (Pbest, gbest) , 3 10 d DCJ (Pbest, gbest) , 4 10 d DCJ (Pbest, gbest) , 5 10 d DCJ (Pbest, gbest) , 6 10 d DCJ (Pbest, gbest) steps away from Pbest to gbest. Then, we randomly selected one genome from these six candidates to constitute the intermediate best median genome.…”

Section: Update the Intermediate Best Median Genomes ( P D ) By Adoptmentioning

confidence: 99%

“…Advancements in the reconstruction of ancestral genomes constitute significant developments in Bioinformatics. Inferred ancestral genomes can be used to reconstruct deep evolutionary histories, which in turn, have been successfully applied to a wide range of problems facing contemporary society, including in controlling hereditary diseases, providing a better understanding of complex disease mechanisms for which effective detection and diagnostic pharmaceuticals drugs have then been developed, and inferring epidemic contact structure, among others [1][2][3][4][5]. With the advent of new sequencing techniques in conjunction with sophisticated computing technologies, the availability of whole genome sequences continues to increase.…”

Section: Introductionmentioning

confidence: 99%

Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

et al. 2020

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Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

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Phylogenetic Reconstruction for Copy-Number Evolution Problems

Cited by 2 publications

References 25 publications

Comparing copy-number profiles under multi-copy amplifications and deletions

Comparing copy-number profiles under multi-copy amplifications and deletions

Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

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