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

Rubert, Diego P.; Martinez, Fábio Viduani; Stoye, Jens; Doerr, Daniel

doi:10.1186/s12864-020-6609-x

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…To increase coverage and resolution, we also generated a second set of markers, directly from the genomic sequences and not restricted to genes or CDSs. We used GEESE (Rubert et al, 2020b) to construct genomic markers of length at least 500 bp. GEESE implements a heuristic for the genome segmentation problem (Visnovská et al, 2013) and takes as input local pairwise sequence alignments that we computed with LASTZ.…”

Section: Real Data Analysismentioning

confidence: 99%

Computing the Rearrangement Distance of Natural Genomes

Bohnenkämper

Braga

Doerr

et al. 2021

Journal of Computational Biology

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The computation of genomic distances has been a very active field of computational comparative genomics over the past 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double cut and join distance by Yancopoulos et al. in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao et al. relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an integer linear programming (ILP) solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes that have equal numbers of duplicates of any marker. Therefore, it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this article, we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao et al. to count runs of markers that are under- or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation. The evaluation of our approach shows that it can be used to analyze genomes with up to a few 10,000 markers, which we demonstrate on simulated and real data.

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Section: Real Data Analysismentioning

confidence: 99%

Computing the Rearrangement Distance of Natural Genomes

Bohnenkämper

Braga

Doerr

et al. 2021

Journal of Computational Biology

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“…The computation of the here proposed rearrangement measure depends on pre-defined genomic markers. To obtain such markers from the studied human genome data set, we used GEESE [15]. GEESE implements a heuristic for the genome segmentation problem [19] and takes as input local pairwise sequence alignments.…”

Section: A Analysis Of High-resolution Human Genome Datamentioning

confidence: 99%

Computing the Rearrangement Distance of Natural Genomes

Bohnenkämper¹,

Braga²,

Doerr³

et al. 2020

Lecture Notes in Computer Science

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The computation of genomic distances has been a very active field of computational comparative genomics over the last 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double-cut and join (DCJ) distance by Yancopoulos, Attie and Friedberg in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao, Lin and Moret relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an ILP solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes, that have equal numbers of duplicates of any marker. Therefore it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this paper we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao, Lin and Moret to count runs of markers that are under-or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation.The evaluation of our approach shows that it can be used to analyze genomes with up to a few ten thousand markers, which we demonstrate on simulated and real data. Source code and test data are available from https://gitlab.ub.uni-bielefeld.de/gi/ding. ······ ······ ······ ······ ······ ······ ······ ······ J J J J ······ ······ ········· ········· ········· ········· ········· ········· ········· ········· ········· ········· ········· J J J J ········· ········· ········· ········· J J J J r r r r r r r r r r r r r r r r

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“…Lastly, we have introduced the concept of (family-based) local DCJ similarity, a local genome rearrangement measure analog to local sequence alignment. Then, we have presented ANGORA, an improved workflow for ancestral genome reconstruction from highly diverged genomes such as those of plants, showing how the local DCJ similarity allowed reconstructed ancestral regions with higher resolution [Rubert et al 2020]. Such a workflow relies on an established workflow in the reconstruction of ancestral plants [Salse 2016].…”

Section: Family-based Local Dcj Similaritymentioning

confidence: 99%

“…BMC Genomics, 2020. [Rubert et al 2020] Algorithms for Molecular Biology, BMC Bioinformatics and BMC Genomics are among the most traditional and top indexed journals in bioinformatics.…”

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

Cited by 6 publications

References 32 publications

Computing the Rearrangement Distance of Natural Genomes

Computing the Rearrangement Distance of Natural Genomes

Computing the Rearrangement Distance of Natural Genomes

Distance and Similarity Measures in Comparative Genomics

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